机器学习_boston dataset(波士顿数据集)
深度学习3：波士顿房价预测（1）
深度学习3：波⼠顿房价预测（1）转载：波⼠顿房价问题房价的预测和前两期的问题是不同的，最⼤的区别就是这个问题不是离散的分类，他是⼀个连续值，那么在搭建⽹络时候的技巧就有所区别。
代码实例分析from keras.datasets import boston_housing(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()12导⼊数据train_data.shapetest_data.shape看⼀下数据的尺⼨，发现训练集的尺⼨是404，13；测试集的尺⼨是102，13；说明这些数据不多，这⼗三个数据特征是各种数值，包括犯罪率，住宅平均房间数，道路的通畅程度等。
很明显，这些数据都看起来没什么关系，相互之间⽆法联系，还有⼀个最要命的就是我们⽆法确定那个数据更加的重要。
另外，这些数据的范围也不同，想要使⽤，必须要做⼀些处理。
train_targets看⼀下targets，就可以看到当时房⼦的房价了，这就是训练集中对应的结果集，类似于上两个例⼦中的标签集。
mean = train_data.mean(axis=0)train_data -= meanstd = train_data.std(axis=0)train_data /= stdtest_data -= meantest_data /= std这⾥就是应对数据范围不同的办法，⽅法叫标准化，含义就是加⼯每个特征，使他们的数据满⾜平均值为0，标准差为1.具体的⽅法就是每列取平均值，减去平均值，再除以减掉之后的标准差。
这⾥要注意标准化所⽤的数据必须是在训练集上得到，实际操作中不能让任何数据从验证集上得到，不然会导致模型过拟合的问题。
from keras import modelsfrom keras import layersdef build_model():model = models.Sequential()model.add(layers.Dense(64, activation='relu',input_shape=(train_data.shape[1],)))model.add(layers.Dense(64, activation='relu'))model.add(layers.Dense(1))pile(optimizer='rmsprop', loss='mse', metrics=['mae'])return model这⾥就是搭建学习模型的步骤，因为这个模型要重复使⽤，所以我们把它写成函数的形式。
作业-机器学习-波士顿房价预测四种回归算法
作业-机器学习-波⼠顿房价预测四种回归算法# -*- coding:utf-8 -*-#基于波⼠顿房屋租赁数据进⾏房屋租赁价格预测模型构建，使⽤lasso回归算法做特征选择后，分别使⽤线性回归，#Lasso回归， Ridge回归， ElasticNet四类回归算法构建模型（分别测试1，2，3阶）import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltimport pandas as pdimport warningsimport sklearnfrom sklearn.linear_model import LinearRegression,LassoCV,RidgeCV,ElasticNetCVfrom sklearn.preprocessing import PolynomialFeatures #多项式特征from sklearn.pipeline import Pipelinefrom sklearn.linear_model.coordinate_descent import ConvergenceWarning #拦截异常的from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.grid_search import GridSearchCV #从sklearn.grid_search中导⼊⽹格搜索模块GridSearchCV。
from sklearn import metrics #评价指标def notEmpty(s):return s !='' #是空的话就是FLASE，不是空的话就是TRUE#设置字符集，防⽌中⽂乱码mpl.rcParams['font.sans-serif']=[u'simHei']mpl.rcParams['axes.unicode_minus']=False#拦截异常warnings.filterwarnings(action = 'ignore', category=ConvergenceWarning)# 加载数据names = ['CRIM','ZN', 'INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT'] #前13个和房价相关的字段，LSTAT为房价path = "datas/boston_housing.data"# 由于数据⽂件格式不统⼀，所以读取的时候，先按照⼀⾏⼀个字段属性读取数据，然后再安装每⾏数据进⾏处理fd = pd.read_csv(path,header=None)#print(fd.shape)data = np.empty((len(fd),14)) # len(fd)⾏，14列for i, d in enumerate(fd.values): #enumerate⽣成⼀列索引i,d为其元素d = map(float,filter(notEmpty,d[0].split(' '))) #filter⼀个函数，⼀个list, 就是空的扔掉，有值的留下#根据函数结果是否为真，来过滤list中的项data[i]=list(d)#分割数据x,y = np.split(data,(13,),axis=1) #分割前13列数据# print(x[0:5])#print(y) 由于y是个⼆维的，所以要⽤ravel拉成⼀维的y = y.ravel() #转换格式拉直操作#print(y[0:5])ly=len(y)# print(y.shape)print('样本数据量:%d,特征个数:%d '%x.shape)print('target样本数据量:%d'%y.shape[0])#Pipeline常⽤于并⾏调参models = [Pipeline([('ss', StandardScaler()),('poly', PolynomialFeatures()),('linear', RidgeCV(alphas=np.logspace(-3,1,20)))]),Pipeline([('ss', StandardScaler()),('poly', PolynomialFeatures()),('linear', LassoCV(alphas=np.logspace(-3,1,20))) #logspace 以10为底，从10的-3次⽅⽌10的0次⽅，中间有20步]),Pipeline([('ss', StandardScaler()),('poly', PolynomialFeatures()),('linear', LinearRegression())]),Pipeline([('ss', StandardScaler()),('poly', PolynomialFeatures()),('linear', ElasticNetCV(alphas=np.logspace(-3,1,20)))])]#参数字典，字典中的key是属性的名称，value是可选的参数列表parameters = {"poly__degree": [3,2,1],"poly__interaction_only": [True, False],#只产⽣交互相选TRUE，得到[0次⽅，X本⾝，Y本⾝，X1*Y1] ；默认选FLASE，不仅产⽣交互项，如X1*X1,Y1*Y1也会有"poly__include_bias": [True, False], #多项式幂为零的特征作为线性模型中的截距,默认为True"linear__fit_intercept": [True, False]}# rf = PolynomialFeatures(2,interaction_only=True)# a = pd.DataFrame({# 'name':[1,2,3,4,5],# 'score':[2,3,4,4,5]# })# b=rf.fit_transform(a)# print(b)#数据分割x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)#Lasso和Ridge模型⽐较运⾏图表展⽰titles = ['Ridge','Lasso','LinearRegression','ElasticNet']colors = ['g-','b-','y-','c-']plt.figure(figsize=(16,8), facecolor='w')ln_x_test = range(len(x_test))plt.plot(ln_x_test, y_test,'r-',lw=2,label=u'真实值')for t in range(4):#获取模型并设置参数#GridSearchCV: 进⾏交叉验证，选择出最优的参数值出来#第⼀个输⼊参数：进⾏参数选择的模型，#param_grid：⽤于进⾏模型选择的参数字段，要求是字典类型；cv: 进⾏⼏折交叉验证model = GridSearchCV(models[t], param_grid=parameters, cv=5, n_jobs=1) #五折交叉验证#模型训练-⽹格搜索model.fit(x_train, y_train)#模型效果值获取（最优参数）print('%s算法：最优参数：'%titles[t],model.best_params_)print('%s算法：R值=%.3f'%(titles[t],model.best_score_))#模型预测y_predict=model.predict(x_test)#画图plt.plot(ln_x_test,y_predict,colors[t],lw=2,label=u'%s算法估计值，$R^2$=%.3f'%(titles[t],model.best_score_)) #图形显⽰plt.legend(loc='lower right')plt.grid(True)plt.title(u'波⼠顿房屋价格预测')plt.show()## 模型训练 ====> 单个Lasso模型（1-3阶特征选择）<degree参数给定1-3阶情况的最优参数>for e in range(1,4):model = Pipeline([('ss', StandardScaler()),('poly', PolynomialFeatures(degree=e, include_bias=True, interaction_only=True)),('linear', LassoCV(alphas=np.logspace(-3,1,20), fit_intercept=False))])# 模型训练model.fit(x_train, y_train)# 模型评测## 数据输出print ("%d阶参数:"%e, list(zip(names,model.get_params('linear')['linear'].coef_)))print ("%d阶截距:"%e, model.get_params('linear')['linear'].intercept_)。
生物大数据处理中的机器学习算法与实例解析
生物大数据处理中的机器学习算法与实例解析随着科技的快速发展，生物学研究中产生了大量的数据，如基因组测序数据、转录组数据和蛋白质结构数据等。
这些生物大数据对于解析生物体结构与功能，以及疾病的发生机制等方面具有重要意义。
然而，由于数据量庞大、维度高、复杂性强等特点，如何高效地处理和分析这些生物大数据成为了一个挑战。
机器学习算法在生物大数据处理中发挥了重要的作用，它可以帮助研究人员从复杂的生物数据中挖掘出有价值的信息。
本文将对生物大数据处理中常用的机器学习算法进行详细解析，并给出一些实例应用。
一、支持向量机（Support Vector Machine，SVM）支持向量机是一种将输入数据映射到高维空间的非线性分类算法。
在生物大数据处理中，支持向量机常被用来进行分类和预测分析。
例如，在癌症研究中，可以利用支持向量机通过肿瘤标记物的信息来进行肿瘤类型的分类。
此外，支持向量机还可以应用于基因表达数据的分类和特征选择。
二、随机森林（Random Forest）随机森林是一种集成学习算法，它通过构建多个决策树来进行分类和预测。
在生物大数据处理中，随机森林经常被用来进行基因表达数据的分类，以及蛋白质折叠状态的预测等。
例如，在药物研发中，可以利用随机森林算法进行药物作用的预测。
三、深度学习（Deep Learning）深度学习是一种基于神经网络的机器学习方法，它在生物大数据处理中展现了强大的能力。
深度学习可用于图像分析、序列分析等多个方面。
例如，在图像识别中，深度学习可以用于细胞图像的分割和分类。
此外，在基因组学研究中，深度学习还可以用于DNA序列的注释和基因识别等任务。
四、聚类分析（Clustering）聚类分析是一种无监督学习算法，它将数据集中具有相似特征的对象归为一类。
在生物大数据处理中，聚类分析常常用于发现生物样本的表型模式、基因调控网络的构建等。
例如，在单细胞转录组测序数据分析中，可以利用聚类分析识别出具有相似表达谱的细胞群，并进行细胞类型的分类。
如何利用机器学习进行生物信息学数据分析(九)
近年来，生物信息学领域的迅速发展使得研究人员能够更好地理解生物系统的复杂性。
在生物信息学研究中，大量的生物数据需要进行分析和解释，而机器学习技术的应用为这一过程提供了新的可能性。
本文将探讨如何利用机器学习技术进行生物信息学数据分析，以及机器学习在生物信息学研究中的应用。
一、生物信息学数据的特点生物信息学数据通常具有高维度、复杂性和多样性的特点。
例如，基因组学数据包括基因序列、基因表达数据和遗传变异等多种类型的信息。
传统的统计学方法在处理这些数据时往往面临着维度灾难和复杂度问题，而机器学习技术可以通过建立模型来发现数据中的规律和模式，为生物信息学研究提供了新的解决方案。
二、机器学习在生物信息学中的应用在生物信息学研究中，机器学习技术被广泛应用于基因组学、蛋白质组学和代谢组学等领域。
例如，基于机器学习的基因表达数据分析可以帮助研究人员识别潜在的生物标志物和基因调控网络，从而揭示疾病发生和发展的机制。
此外，机器学习算法还可以用于生物序列分析、蛋白质结构预测和代谢物组学数据解释等方面，为生物信息学研究提供了强大的工具支持。
三、常用的机器学习算法在生物信息学数据分析中，常用的机器学习算法包括支持向量机（SVM）、随机森林（Random Forest）、深度学习（Deep Learning）和贝叶斯网络等。
这些算法具有不同的特点和适用范围，研究人员可以根据具体的数据类型和研究目的选择合适的算法进行分析和建模。
四、生物信息学数据分析的挑战和发展趋势尽管机器学习技术在生物信息学数据分析中取得了显著的进展，但仍然面临着一些挑战。
例如，生物信息学数据的质量和标注问题、样本量不足和数据集偏差等都会影响机器学习模型的性能和稳定性。
未来，研究人员需要进一步开发新的机器学习算法和工具，以应对生物信息学数据分析中的挑战，并不断提升分析的准确性和可靠性。
综上所述，机器学习技术在生物信息学数据分析中扮演着重要的角色，为研究人员提供了强大的工具和方法来探索生物系统的复杂性。
机器学习知识：机器学习中的多模态数据处理
机器学习知识：机器学习中的多模态数据处理随着互联网的飞速发展，我们的生活中产生了越来越多的数据，其中包含了不同来源、不同形式和不同特征的多模态数据。
多模态数据是指一组数据中包含有两种或以上不同类型的数据，如图像、语音、文本、传感器数据等。
如何在处理这些多模态数据的过程中提高高效性，成了机器学习领域的一个重要问题。
在过去的数年中，解决多模态数据处理问题的研究进展迅猛，已经涌现出多种方法。
其中一些方法是针对特定的领域进行的，比如图像识别、语音识别、语义分析等；还有一些方法是通用的，适用于任何多模态数据处理场景。
多模态数据处理的优势与单一数据模态（如图像、文本或声音）相比，多模态数据有着更为丰富的信息。
它们能包含来自不同传感器和不同模态的信息，这些信息能够一起协同工作，形成更加全面、准确的信息图像。
同时，在数据预处理和模型训练方面使用多模态数据有多种优势。
首先，多模态数据能够提高模型的鲁棒性。
在真实的场景下，多样性的信息输入可以减少噪声的作用，从而使模型更有鲁棒性。
由于多模态数据能够提供更加全面准确的输入，不仅有利于模型的训练，同时也可以提高模型的准确性。
其次，使用多模态数据能够提高神经网络的效率。
传统的单模态神经网络处理单一类型数据时还不能达到理想状态的准确率。
而多模态数据的结合就可以从数据方面提高神经网络训练的效率。
多模态数据处理的难点尽管多模态数据具有很多优势，但是如何处理多模态数据却是相对困难的。
因为不同模态的数据可能具有不同的特征，可能需要采取不同的方式进行处理和分析。
其中，最大的困难之一是如何统一多模态数据的表达方式。
将不同类型的数据转换为通用的表示形式是多模态数据处理中的一个挑战。
有许多方法可以处理多模态数据，主要分为两类：基于特征的方法和基于模型的方法。
基于特征的方法基于特征的方法是最普遍、常见的方法。
该方法的核心思路是从多个模态中提取特征，然后将这些特征结合在一起形成一个通用的特征表示。
s k l e a r n 介 绍 ( 2 0 2 0 )
Sklearn_工具--2SKlearn介绍SKlearn介绍一.Python科学计算环境Final二.SKlearn算法库的顶层设计 1.SKlearn包含哪些模块 2.SKlearn六大板块统一API2.1API2.2sklearn监督学习工作流程2.2sklearn无监督学习工作流程2.3sklearn数据预处理工作流程 2.4SKlearn算法模块的学习顺序三.SKlearn数据集操作API1.自带小数据集1.1鸢尾花数据集1.2手写数字数据集:load_digits()1.3乳腺癌数据集:load_breast_cancer()1.4糖尿病数据集:load_diabetes()1.4波士顿房价数据集:load_boston()1.5体能训练数据集:load_linnerud()1.6图像数据集:load_sample_image(name)2.svmlight-libsvm格式的数据集3.可在线下载的数据集(Downloadable Dataset）3.1 20类新闻文本数据集3.2 野外带标记人脸数据集:fetch_lfw_people()-fetch_lfw_pairs()3.3Olivetti人脸数据集:fetch_olivetti_faces()3.4rcv1多标签数据集:fetch_rcv1()3.5Forest covertypes:预测森林表面植被类型4计算机生成的数据集 4.1用于分类任务和聚类任务的4.2make_multilabel_classification,多标签随机样本4.3用于回归任务的4.4用于流形学习的4.4用于因子分解的一.Py【现场实操追-女教-程】thon科学计算环境FinalScik【QQ】it-Image是专门用来处理图像的机器学习接口处理图【１】像的还有OpenCV，OpenCV使用c和c++写的，但是提供了py【О】thon接口，可以用python去调用二.SK【⒈】learn算法库的顶层设计科学包【６】是如何架构起来的1.S【⒐】Klearn包含哪些模块SKl【⒌】earn监督学习模块有15种SKle【２】arn无监督学习模块SKle【б】arn数据变换模块管道流pipline严格来说不是数据变换模块，pipline负责输出重定向，sklearn通过pipline可以将train，test，得分估计连成一个一长串的，方便整理代码。
机器学习_Airline Dataset(航空公司数据集)
Airline Dataset(航空公司数据集)数据摘要：An airline provides air transport services for passengers or freight. Airlines lease or own their aircraft with which to supply these services and may form partnerships or alliances with other airlines for mutual benefit. Generally, airline companies are recognized with an air operating certificate or license issued by a governmental aviation body.Airlines vary from those with a single aircraft carrying mail or cargo, through full-service international airlines operating hundreds of aircraft. Airline services can be categorized as being intercontinental,intra-continental, domestic, or international, and may be operated as scheduled services or charters.中文关键词：航空,数据集,机器学习,分类,英文关键词：Airline,dataset,Machine Learning,Classification,数据格式：TEXT数据用途：Information Processing Classification数据详细介绍：AirlineAn airline provides air transport services for passengers or freight. Airlines lease or own their aircraft with which to supply these services and may form partnerships or alliances with other airlines for mutual benefit. Generally, airline companies are recognized with an air operating certificate or license issued by a governmental aviation body.Airlines vary from those with a single aircraft carrying mail or cargo, through full-service international airlines operating hundreds of aircraft. Airline services can be categorized as being intercontinental, intra-continental, domestic, or international, and may be operated as scheduled services or charters.History This section does not cite any references or sources.Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (July 2008)[edit] The first airlinesFailed attempt at an airline before DELAGAmericans, such as Rufus Porter and Frederick Marriott, attempted to start airlines using airships in the mid-19th century, focusing on the New York–California route. Those attempts floundered due to such mishaps as the airships catching fire and the aircraft being ripped apart by spectators. DELAG, DeutscheLuftschiffahrts-Aktiengesellschaft was the world's first airline.[3] It was founded on November 16, 1909 with government assistance, and operated airships manufactured by The Zeppelin Corporation. Its headquarters were in Frankfurt. The four oldest non-dirigible airlines that still exist are Netherlands' KLM, Colombia's Avianca, Australia's Qantas, and the Czech Republic's Czech Airlines. KLM first flew in May 1920, while Qantas (which stands for Queensland and Northern Territory Aerial Services Limited) was founded in Queensland, Australia, in late 1920.[edit] U.S. airline industry[edit] Early developmentTWA Douglas DC-3 in 1940. The DC-3, often regarded as one of the most influential aircraft in the history of commercial aviation, revolutionized the aviation industry.[4]Tony Jannus conducted the United State's first scheduled commercial airline flight on 1 January 1914 for the St. Petersburg-Tampa Airboat Line.[5] The 23-minute flight traveled between St. Petersburg, Florida and Tampa, Florida, passing some 50 feet (15 m) above Tampa Bay in Jannus' Benoist XIV biplane flying boat. Chalk's International Airlines began service between Miami and Bimini in the Bahamas in February 1919. Based in Ft. Lauderdale, Chalk's claimed to be the oldest continuously operating airline in the United States until its closure in 2008.[6]Following World War I, the United States found itself swamped with aviators. Many decided to take their war-surplus aircraft on barnstorming campaigns, performing acrobatic maneuvers to woo crowds. In 1918, the United States Postal Service won the financial backing of Congress to begin experimenting with air mail service, initially using Curtiss Jenny aircraft that had been procured by the United States Army for reconnaissance missions on the Western Front. Private operators were the first to fly the mail but due to numerous accidents the US Army was tasked with mail delivery. During the course of the Army's involvement they proved to be too unreliable and lost their air mail duties. By the mid-1920s, the Postal Service had developed its own air mail network, based on a transcontinental backbone between New York and San Francisco. To supplant this service, they offered twelve contracts for spur routes to independent bidders. Some of the carriers that won these routes would, through time and mergers, evolve intoPan Am, Delta Air Lines, Braniff Airways, American Airlines, United Airlines (originally a division of Boeing), Trans World Airlines, Northwest Airlines, and Eastern Air Lines.Service during the early 1920s was sporadic: most airlines at the time were focused on carrying bags of mail. In 1925, however, the Ford Motor Company bought out the Stout Aircraft Company and began construction of the all-metal Ford Trimotor, which became the first successful American airliner. With a 12-passenger capacity, the Trimotor made passenger service potentially profitable. Air service was seen as a supplement to rail service in the American transportation network.At the same time, Juan Trippe began a crusade to create an air network that would link America to the world, and he achieved this goal through his airline, Pan American World Airways, with a fleet of flying boats that linked Los Angeles to Shanghai and Boston to London. Pan Am and Northwest Airways (which began flights to Canada in the 1920s) were the only U.S. airlines to go international before the 1940s.With the introduction of the Boeing 247 and Douglas DC-3 in the 1930s, the U.S. airline industry was generally profitable, even during the Great Depression. This trend continued until the beginning of World War II.[edit] Development since 1945In October 1945, the American Export Airlines became the first airline to offer regular commercial flights between North America and Europe.[7] Shown here is Am Ex Boeing 377 Stratocruiser in 1949.As governments met to set the standards and scope for an emergent civil air industry toward the end of the war, the U.S. took a position of maximum operating freedom; U.S. airline companies were not as hard-hit as European and the few Asian ones had been. This preference for "open skies" operating regimes continues, within limitations, to this day.[citation needed]World War II, like World War I, brought new life to the airline industry. Many airlines in the Allied countries were flush from lease contracts to the military, and foresaw a futureexplosive demand for civil air transport, for both passengers and cargo. They were eager to invest in the newly emerging flagships of air travel such as the Boeing Stratocruiser, Lockheed Constellation, and Douglas DC-6. Most of these new aircraft were based on American bombers such as the B-29, which had spearheaded research into new technologies such as pressurization. Most offered increased efficiency from both added speed and greater payload.In the 1950s, the De Havilland Comet, Boeing 707, Douglas DC-8, and Sud Aviation Caravelle became the first flagships of the Jet Age in the West, while the Soviet Union bloc had Tupolev Tu-104 and Tupolev Tu-124 in the fleets of state-owned carriers such as Czechoslovak ČSA, Soviet Aeroflot and East-German Interflug. The Vickers Viscount and Lockheed L-188 Electra inaugurated turboprop transport.The next big boost for the airlines would come in the 1970s, when the Boeing 747, McDonnell Douglas DC-10, and Lockheed L-1011 inaugurated widebody ("jumbo jet") service, which is still the standard in international travel. The Tupolev Tu-144 and its Western counterpart, Concorde, made supersonic travel a reality. Concorde first flew in 1969 and operated through 2003. In 1972, Airbus began producing Europe's most commercially successful line of airliners to date. The added efficiencies for these aircraft were often not in speed, but in passenger capacity, payload, and range. Airbus also features modern electronic cockpits that were common across their aircraft to enable pilots to fly multiple models with minimal cross-training.Pan Am Boeing 747 Clipper Neptune's Car in 1985. The deregulation of the American airline industry increased the financial troubles of the iconic airline which ultimately filed for bankruptcy in December 1991.[8]1978's U.S. airline industry deregulation lowered barriers for new airlines just as a downturn occurred. New start-ups entered during the downturn, during which time they found aircraft and funding, contracted hangar and maintenance services, trained new employees, and recruited laid off staff from other airlines.As the business cycle returned to normalcy, major airlines dominated their routes through aggressive pricing and additional capacity offerings, often swamping new startups. Only America West Airlines (which has since merged with US Airways) remained a significant survivor from this new entrant era, as dozens, even hundreds, have gone under.In many ways, the biggest winner in the deregulated environment was the air passenger. Indeed, the U.S. witnessed an explosive growth in demand for air travel, as many millions who had never or rarely flown before became regular fliers, even joining frequent flyer loyalty programs and receiving free flights and other benefits from their flying. New services and higher frequencies meant that business fliers could fly to another city, do business, and return the same day, for almost any point in the country. Air travel's advantages put intercity bus lines under pressure, and most have withered away.By the 1980s, almost half of the total flying in the world took place in the U.S., and today the domestic industry operates over 10,000 daily departures nationwide.Toward the end of the century, a new style of low cost airline emerged, offering a no-frills product at a lower price. Southwest Airlines, JetBlue, AirTran Airways, Skybus Airlines and other low-cost carriers began to represent a serious challenge to the so-called "legacy airlines", as did their low-cost counterparts in many other countries. Their commercial viability represented a serious competitive threat to the legacy carriers. However, of these, ATA and Skybus have since ceased operations.Increasingly since 1978, US airlines have been reincorporated and spun off by newly created and interally led manangement companies, and thus becoming nothing more than operating units and subsidiaries with limited finanically decisive control. Among some of these holding companies and parent companies that are the relatively well known, are the UAL Corporation, along with the AMR Corporation, among a long list of airline holding companies sometime recognized world wide. Less recognized are the private equity firms which often seize managerial, financial, and board of directors control of distressed airlinecompanies by temporarily investing large sums of capital in air carriers, so as to rescheme an airlines assets into a profitable organization or liquidating an air carrier of their profitable and worthwhile routes and business operations.Thus the last 50 years of the airline industry have varied from reasonably profitable, to devastatingly depressed. As the first major market to deregulate the industry in 1978, U.S. airlines have experienced more turbulence than almost any other country or region. Today, American Airlines is the only U.S. legacy carrier to survive bankruptcy-free.[edit] The Airline Industry BailoutCongress passed the Air Transportation Safety and System Stabilization Act (P.L. 107-42) in response to a severe liquidity crisis facing the already-troubled airline industry in the aftermath of the September 11th terrorist attacks. Congress sought to provide cash infusions to carriers for both the cost of the four-day federal shutdown of the airlines and the incremental losses incurred through December 31, 2001 as a result of the terrorist attacks. This resulted in the first government bailout of the 21st century.[9]In recognition of the essential national economic role of a healthy aviation system, Congress authorized partial compensation of up to $5 billion in cash subject to review by the Department of Transportation and up to $10 billion in loan guarantees subject to review by a newly created Air Transportation Stabilization Board (ATSB). The applications to DOT for reimbursements were subjected to rigorous multi-year reviews not only by DOT program personnel but also by the Government Accountability Office [10] and the DOT Inspector General.[11][12]Ultimately, the federal government provided $4.6 billion in one-time, subject-to-income-tax cash payments to 427 U.S. air carriers, with no provision for repayment, essentially a gift from the taxpayers. (Passenger carriers operating scheduled service received approximately $4 billion, subject to tax.) [13] In addition, the ATSB approved loan guarantees to six airlines totaling approximately $1.6 billion. Data from the Treasury Department show that the government recouped the $1.6 billion and a profit of $339million from the fees, interest and purchase of discounted airline stock associated with loan guarantees.[14][edit] European airline industryThe Imperial Airways Empire Terminal, Victoria, London. Trains ran from here to flying boats in Southampton, and to Croydon Airport.The first countries in Europe to embrace air transport were Austria, Belgium, Finland, France, Germany, the Netherlands and the United Kingdom.Austria initiated the first regularly scheduled airmail service on March 31, 1918 in the midst of World War I. The route provided airmail service spanning Vienna to Krakow (now in Poland) to Lviv (now in Ukraine), as was often also extended to Kiev andOdessa.[15][16]KLM, the oldest carrier still operating under its original name, was founded in 1919. The first flight (operated on behalf of KLM by Aircraft Transport and Travel) transported two English passengers to Schiphol, Amsterdam from London in 1920. Like other major European airlines of the time (see France and the UK below), KLM's early growth depended heavily on the needs to service links with far-flung colonial possessions (Dutch Indies). It is only after the loss of the Dutch Empire that KLM found itself based at a small country with few potential passengers, depending heavily on transfer traffic, and was one of the first to introduce the hub-system to facilitate easy connections.France began an air mail service to Morocco in 1919 that was bought out in 1927, renamed Aéropostale, and injected with capital to become a major international carrier. In 1933, Aéropostale went bankrupt, was nationalized and merged with several other airlines into what became Air France.In Finland, the charter establishing Aero O/Y (now Finnair) was signed in the city of Helsinki on September 12, 1923. Junkers F 13 D-335 became the first aircraft of the company, when Aero took delivery of it on March 14, 1924. The first flight was betweenHelsinki and Tallinn, capital of Estonia, and it took place on March 20, 1924, one week later.Germany's Lufthansa began in 1926. Lufthansa, unlike most other airlines at the time, became a major investor in airlines outside of Europe, providing capital to Varig and Avianca. German airliners built by Junkers, Dornier, and Fokker were the most advanced in the world at the time. In 1931, the airship Graf Zeppelin began offering regular scheduled passenger service between Germany and South America, usually every two weeks, which continued until 1937.[17] In 1936, the airship Hindenburg entered passenger service and successfully crossed the Atlantic 36 times before crashing at Lakehurst, New Jersey on May 6, 1937.[18]The British company Aircraft Transport and Travel commenced a London to Paris service on August 25, 1919, this was the world's first regular international flight. The United Kingdom's flag carrier during this period was Imperial Airways, which became BOAC (British Overseas Airways Co.) in 1939. Imperial Airways used huge Handley-Page biplanes for routes between London, the Middle East, and India: images of Imperial aircraft in the middle of the Rub'al Khali, being maintained by Bedouins, are among the most famous pictures from the heyday of the British Empire.In Soviet Union the Chief Administration of the Civil Air Fleet was established in 1921. One of its first acts was to help found Deutsch-Russische Luftverkehrs A.G. (Deruluft), a German-Russian joint venture to provide air transport from Russia to the West. Domestic air service began around the same time, when Dobrolyot started operations on 15 July 1923 between Moscow and Nizhni Novgorod. Since 1932 all operations had been carried under the name Aeroflot. By the end of the 1930s Aeroflot had become the world's largest airline, employing more than 4,000 pilots and 60,000 other service personnel and operating around 3,000 aircraft (of which 75% were considered obsolete by its own standards). During the Soviet era Aeroflot was synonymous with Russian civil aviation, as it was the only air carrier. It became the first airline in the world to operate sustained regular jet services on 15 September 1956 with the Tupolev Tu-104.[edit] DeregulationDeregulation of the European Union airspace in the early 1990s has had substantial effect on structure of the industry there. The shift towards 'budget' airlines on shorter routes has been significant. Airlines such as EasyJet and Ryanair have grown at the expense of the traditional national airlines.There has also been a trend for these national airlines themselves to be privatised such as has occurred for Aer Lingus and British Airways. Other national airlines, includingItaly's Alitalia, have suffered - particularly with the rapid increase of oil prices in early 2008.[edit] Asian airline industryAlthough Philippine Airlines (PAL) was officially founded on February 26, 1941, its license to operate as an airliner was derived from merged Philippine Aerial Taxi Company (PATCO) established by mining magnate Emmanuel N. Bachrach on December 3, 1930, making it Asia's oldest scheduled carrier still in operation.[19] Commercial air service commenced three weeks later from Manila to Baguio, making it Asia's first airline route. Bachrach's death in 1937 paved the way for its eventual merger with Philippine Airlines in March 1941 and made it Asia's oldest airline. It is also the oldest airline in Asia still operating under its current name.[20] Bachrach's majority share in PATCO was bought by beer magnate Andres R. Soriano in 1939 upon the advice of General Douglas McArthur and later merged with newly formed Philippine Airlines with PAL as the surviving entity. Soriano has controlling interest in both airlines before the merger. PAL restarted service on March 15, 1941 with a single Beech Model 18 NPC-54 aircraft, which started its daily services between Manila (from Nielson Field) and Baguio, later to expand with larger aircraft such as the DC-3 and Vickers Viscount.India was also one of the first countries to embrace civil aviation.[21] One of the first West Asian airline companies was Air India, which had its beginning as Tata Airlines in 1932, a division of Tata Sons Ltd. (now Tata Group). The airline was founded by India's leading industrialist, JRD Tata. On October 15, 1932, J. R. D. Tata himself flew a single engined De Havilland Puss Moth carrying air mail (postal mail of Imperial Airways) from Karachi toMumbai via Ahmedabad. The aircraft continued to Madras via Bellary piloted by Royal Air Force pilot Nevill Vintcent . Tata Airlines was also one of the world's first major airlines which began its operations without any support from the Government.[22]With the outbreak of World War II, the airline presence in Asia came to a relative halt, with many new flag carriers donating their aircraft for military aid and other uses. Following the end of the war in 1945, regular commercial service was restored in India and Tata Airlines became a public limited company on July 29, 1946 under the name Air India. After the independence of India, 49% of the airline was acquired by the Government of India. In return, the airline was granted status to operate international services from India as the designated flag carrier under the name Air India International.On July 31, 1946, a chartered Philippine Airlines (PAL) DC-4 ferried 40 American servicemen to Oakland, California from Nielson Airport in Makati City with stops in Guam, Wake Island, Johnston Atoll and Honolulu, Hawaii, making PAL the first Asian airline to cross the Pacific Ocean. A regular service between Manila and San Francisco was started in December. It was during this year that the airline was designated as the flag carrier of Philippines.During the era of decolonization, newly-born Asian countries started to embrace air transport. Among the first Asian carriers during the era were Cathay Pacific of Hong Kong (founded in September 1946), Orient Airways (later Pakistan International Airlines; founded in October 1946), Malayan Airlines (later Singapore and Malaysia Airlines; founded in 1947), El Al in Israel in 1948, Garuda Indonesia in 1949, Japan Airlines in 1951, and Korean Air in 1962.[edit] Latin American airline industryTAM Airlines is the largest airline in Latin America in terms of number of annual passengers flown.[23]Among the first countries to have regular airlines in Latin America were Colombia with Avianca, Brazil with Varig, Chile with LAN Chile (today LAN Airlines), Dominican Republic with Dominicana de Aviación, Mexico with Mexicana de Aviación,and TACA as a brand of several airlines of Central American countries (Honduras, El Salvador, Costa Rica, Guatemala and Nicaragua). All the previous airlines started regular operations before World War II.The air travel market has evolved rapidly over recent years in Latin America. Some industry estimations over 2000 new aircraft will begin service over the next five years in this region.[citation needed]These airlines serve domestic flights within their countries, as well as connections within Latin America and also overseas flights to North America, Europe, Australia, Africa and Asia.Just three airlines: LAN (Latin American Networks), Oceanair and TAM Airlines have international subsidiaries with Chile as the central operation along with Peru, Ecuador, Argentina and some operations in the Dominican Republic and TAM with TAM Mercosur have a base in Asuncion, Paraguay. Avianca have the control of Oceanair, VIP Airlines and also have an estrategic alliance with TACA.The three main hubs in Latin America are Mexico City in Mexico, São Paulo in Brazil and Santiago in Chile.[edit] Regulatory considerations[edit] NationalGaruda Indonesia Boeing 747-400 parked at Narita International Airport. This Indonesian Flag carrier is wholly owned by the Indonesian GovernmentMany countries have national airlines that the government owns and operates. Fully private airlines are subject to a great deal of government regulation for economic, political, and safety concerns. For instance, governments often intervene to halt airline labor actions in order to protect the free flow of people, communications, and goods between different regions without compromising safety.The United States, Australia, and to a lesser extent Brazil, Mexico, India, the United Kingdom and Japan have "deregulated" their airlines. In the past, these governments dictated airfares, route networks, and other operational requirements for each airline. Since deregulation, airlines have been largely free to negotiate their own operating arrangements with different airports, enter and exit routes easily, and to levy airfares and supply flights according to market demand.Cyprus Airways national airline of CyprusThe entry barriers for new airlines are lower in a deregulated market, and so the U.S. has seen hundreds of airlines start up (sometimes for only a brief operating period). This has produced far greater competition than before deregulation in most markets, and average fares tend to drop 20% or more. The added competition, together with pricing freedom, means that new entrants often take market share with highly reduced rates that, to a limited degree, full service airlines must match. This is a major constraint on profitability for established carriers, which tend to have a higher cost base.As a result, profitability in a deregulated market is uneven for most airlines. These forces have caused some major airlines to go out of business, in addition to most of the poorly established new entrants.[edit] InternationalSingapore Airlines Airbus A380 lands at Changi Airport. Singapore Airlines was the first international airline to operate the A380, the world's largest passenger airliner.[24]Groups such as the International Civil Aviation Organization establish worldwide standards for safety and other vital concerns. Most international air traffic is regulated by bilateral agreements between countries, which designate specific carriers to operate on specific routes. The model of such an agreement was the Bermuda Agreement between the US and UK following World War II, which designated airports to be used for transatlantic flights and gave each government the authority to nominate carriers to operate routes.Bilateral agreements are based on the "freedoms of the air", a group of generalized traffic rights ranging from the freedom to overfly a country to the freedom to provide domestic flights within a country (a very rarely granted right known as cabotage). Most agreements permit airlines to fly from their home country to designated airports in the other country: some also extend the freedom to provide continuing service to a third country, or to another destination in the other country while carrying passengers from overseas.In the 1990s, "open skies" agreements became more common. These agreements take many of these regulatory powers from state governments and open up international routes to further competition. Open skies agreements have met some criticism, particularly within the European Union, whose airlines would be at a comparative disadvantage with the United States' because of cabotage restrictions.[edit] Economic considerationsJuan Trippe, the founder of Pan American World Airways, surveying his globe. The collapse of Pan Am, an airline often credited for shaping the international airline industry, in December 1991 highlighted the financial complexities faced by major airline companies.Historically, air travel has survived largely through state support, whether in the form of equity or subsidies. The airline industry as a whole has made a cumulative loss during its 100-year history, once the costs include subsidies for aircraft development and airport construction.[25][26]One argument is that positive externalities, such as higher growth due to global mobility, outweigh the microeconomic losses and justify continuing government intervention. A historically high level of government intervention in the airline industry can be seen as part of a wider political consensus on strategic forms of transport, such as highways and railways, both of which receive public funding in most parts of the world. Profitability is likely to improve in the future as privatization continues and more competitive low-cost carriers proliferate.[citation needed]Although many countries continue to operate state-owned or parastatal airlines, many large airlines today are privately owned and are therefore governed by microeconomic principles in order to maximize shareholder profit.[edit] Ticket revenueAirlines assign prices to their services in an attempt to maximize profitability. The pricing of airline tickets has become increasingly complicated over the years and is now largely determined by computerized yield management systems.Because of the complications in scheduling flights and maintaining profitability, airlines have many loopholes that can be used by the knowledgeable traveler. Many of these airfare secrets are becoming more and more known to the general public, so airlines are forced to make constant adjustments.Most airlines use differentiated pricing, a form of price discrimination, in order to sell air services at varying prices simultaneously to different segments. Factors influencing the price include the days remaining until departure, the booked load factor, the forecast of total demand by price point, competitive pricing in force, and variations by day of week of departure and by time of day. Carriers often accomplish this by dividing each cabin of the aircraft (first, business and economy) into a number of travel classes for pricing purposes.A complicating factor is that of origin-destination control ("O&D control"). Someone purchasing a ticket from Melbourne to Sydney (as an example) for AU$200 is competing with someone else who wants to fly Melbourne to Los Angeles through Sydney on the same flight, and who is willing to pay AU$1400. Should the airline prefer the $1400 passenger, or the $200 passenger plus a possible Sydney-Los Angeles passenger willing to pay $1300? Airlines have to make hundreds of thousands of similar pricing decisions daily.Lufthansa Boeing 747-400.The advent of advanced computerized reservations systems in the late 1970s, most notably Sabre, allowed airlines to easily perform cost-benefit。
数据分析知识：数据分析中的深度置信网络
数据分析知识：数据分析中的深度置信网络深度置信网络是一种非监督学习算法，用于对大规模非标记数据进行分析和建模。
该算法由Hinton等人于2006年提出，并在后续的研究中不断优化和扩展。
深度置信网络在图像处理、语音识别、自然语言处理及推荐系统等领域都有广泛的应用。
深度置信网络（Deep Belief Networks，简称DBN）由多个堆叠的受限玻尔兹曼机（Restricted Boltzmann Machines，简称RBM）组成，每个RBM通过学习数据的概率分布，能够将高维复杂的输入数据映射到低维特征空间中，并提取数据的潜在结构。
深度置信网络包含多个隐层，每个隐层都是上一个隐层的输入，最后一层输出的结果则会被用作分类或回归分析的输入。
深度置信网络的训练分为两个阶段：预训练和微调。
预训练阶段是指，以无监督的方式对每个RBM进行训练，将其中的权值和偏置逐层初始化，从而学习低层特征。
该过程可以使用反向传播算法实现，有效地解决了传统神经网络在处理大规模非标记数据时遇到的问题。
微调阶段则是在预训练的基础上，以有监督的方式进行全局优化，调整深度置信网络中的超参数，如学习率、激活函数等，使得网络能够更准确地预测数据的标签，并具有更好的泛化能力。
深度置信网络的优点在于它可以处理高维度的复杂数据，如图像、语音、文本等。
此外，它还可以避免过度拟合、提高模型的泛化能力和减小数据降维误差。
深度置信网络在图像识别、人脸识别、自然语言处理等方面的应用效果显著，已成为计算机科学中热门的研究方向之一。
然而，深度置信网络也存在一些挑战和限制。
首先，深度置信网络的训练过程是计算密集型的，需要大量计算资源和时间。
此外，当处理非线性问题时，深度置信网络需要足够多的训练数据，否则就容易发生过拟合现象。
此外，如果深度置信网络的层数过多，容易出现梯度消失或梯度爆炸等问题，导致模型性能下降。
因此，在实践中需要仔细设计网络结构，并进行超参数和训练策略的优化。
机器学习》20-实验三 波士顿房价预测参考代码[3页]
![机器学习》20-实验三 波士顿房价预测参考代码[3页]](https://img.taocdn.com/s3/m/15c4e6feac51f01dc281e53a580216fc700a5398.png)
# 从sklearn.datasets导入波士顿房价数据from sklearn.datasets import load_boston# 读取房价数据存储在变量X,y中X,y = load_boston(return_X_y=True)print(X.shape)print(y.shape)# 数据分割from sklearn.model_selection import train_test_split# 70%作为训练样本，30%数据作为测试样本X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)print(X_train.shape)print(X_test.shape)print(y_train.shape)print(y_test.shape)# 数据标准化from sklearn.preprocessing import StandardScalerscaler_X = StandardScaler()scaler_y = StandardScaler()# 分别对训练和测试数据的特征以及目标值进行标准化处理X_train = scaler_X.fit_transform(X_train)y_train = scaler_y.fit_transform(y_train.reshape(-1, 1))X_test = scaler_X.transform(X_test)y_test = scaler_y.transform(y_test.reshape(-1, 1))# 从sklearn.linear_model导入LinearRegressionfrom sklearn.linear_model import LinearRegression# 使用默认参数值实例化线性回归器LinearRegressionlr = LinearRegression()# 使用训练数据进行训练lr.fit(X_train, y_train)# 对测试数据进行回归预测lr_y = lr.predict(X_test)# 导入r2_score、mean_squared_error以及mean_absolute_error from sklearn.metrics import r2_scoreprint("LinearRegression的R_squared：",r2_score(y_test, lr_y))from sklearn.metrics import mean_squared_errorprint("LinearRegression均方误差：",mean_squared_error(scaler_y.inverse_transform(y_test), scaler_y.inverse_transform(lr_y))) from sklearn.metrics import mean_absolute_errorprint("LinearRegression绝对值误差:",mean_absolute_error(scaler_y.inverse_transform(y_test),scaler_y.inverse_transform(lr_y)))# 使用LinearRegression 自带的评估函数print("LinearRegression自带的评估函数",lr.score(X_test, y_test))print("---" * 20)# 从sklearn.linear_model导入SGDRegressorfrom sklearn.linear_model import SGDRegressorsgdr = SGDRegressor(max_iter=5, tol=None)# 使用训练数据进行训练sgdr.fit(X_train, y_train.ravel())# 使用SGDRegressor模型自带的评估函数print("SGDRegressor自带的评估函数：",sgdr.score(X_test, y_test))# 从sklearn.neighbors导入KNeighborsRegressorfrom sklearn.neighbors import KNeighborsRegressor# 初始化K近邻回归knr_uni = KNeighborsRegressor(weights="uniform")knr_uni.fit(X_train, y_train.ravel())print('KNeighorRegression（weights="uniform"）自带的评估函数:', knr_uni.score(X_test, y_test))knr_dis = KNeighborsRegressor(weights='distance')# 使用训练数据进行训练knr_dis.fit(X_train, y_train.ravel())print('KNeighorRegression（weights="distance"）自带的评估函数:', knr_dis.score(X_test, y_test))# 房价预测—支持向量回归from sklearn.svm import SVR# 使用SVR训练模型，并对测试数据做出预测svr_linear = SVR(kernel='linear')svr_linear.fit(X_train, y_train.ravel())print('SVR(kernel="linear")自带的评估函数:',svr_linear.score(X_test, y_test))svr_poly = SVR(kernel='poly')svr_poly.fit(X_train, y_train.ravel())print('SVR(kernel="poly")自带的评估函数:',svr_poly.score(X_test, y_test))svr_rbf = SVR(kernel='rbf')svr_rbf.fit(X_train, y_train.ravel())print('SVR(kernel="rbf")自带的评估函数:',svr_rbf.score(X_test, y_test))# 从sklearn.tree中导入DecisionTreeRegressor。
用于人工智能训练的常见数据集及其特点总结
用于人工智能训练的常见数据集及其特点总结随着人工智能技术的迅猛发展，数据集的重要性变得越来越突出。
数据集是人工智能模型训练的基础，它们包含了大量的样本和标签，帮助机器学习算法理解和模拟人类的智能。
在这篇文章中，我们将总结一些常见的用于人工智能训练的数据集及其特点。
1. MNIST手写数字数据集：MNIST是一个经典的数据集，由60000个训练样本和10000个测试样本组成。
每个样本都是一个28x28像素的灰度图像，代表了0到9的手写数字。
这个数据集非常适合用于图像分类任务的初学者，因为它简单易懂，规模适中。
2. CIFAR-10图像分类数据集：CIFAR-10数据集包含了60000个32x32像素的彩色图像，分为10个类别，每个类别有6000个样本。
这个数据集更具挑战性，适合用于图像分类算法的进阶训练。
它的特点是图像质量较高，类别之间的区分度较大。
3. ImageNet图像分类数据集：ImageNet是一个庞大的图像分类数据集，包含了1400万个图像和20000个类别。
这个数据集的规模巨大，涵盖了各种各样的图像，从动物到物体，从自然风景到人物。
ImageNet被广泛应用于深度学习领域，尤其是卷积神经网络的训练。
4. COCO目标检测与分割数据集：COCO数据集是一个用于目标检测和图像分割任务的数据集，包含了超过330000张图像和80个常见对象类别。
这个数据集的特点是图像中包含了多个对象，同时提供了对象的边界框和像素级的分割标注。
COCO数据集对于研究目标检测和图像分割算法非常有价值。
5. Yelp评论情感分析数据集：Yelp评论数据集包含了来自Yelp网站的50000条评论，每条评论都有对应的情感标签（积极或消极）。
这个数据集用于情感分析任务，帮助机器学习算法理解文本中的情感倾向。
它的特点是文本数据，需要使用自然语言处理技术进行特征提取和建模。
6. WMT机器翻译数据集：WMT机器翻译数据集是一个用于机器翻译任务的数据集，包含了来自不同语言的平行文本对。
sklearn提供的自带的数据集
sklearn提供的⾃带的数据集sklearn 的数据集有好多个种⾃带的⼩数据集（packaged dataset）：sklearn.datasets.load_<name>可在线下载的数据集（Downloaded Dataset）：sklearn.datasets.fetch_<name>计算机⽣成的数据集（Generated Dataset）：sklearn.datasets.make_<name>svmlight/libsvm格式的数据集:sklearn.datasets.load_svmlight_file(...)从买了在线下载获取的数据集:sklearn.datasets.fetch_mldata(...)①⾃带的数据集其中的⾃带的⼩的数据集为：sklearn.datasets.load_<name>这些数据集都可以在官⽹上查到，以鸢尾花为例，可以在官⽹上找到demo，from sklearn.datasets import load_iris#加载数据集iris=load_iris()iris.keys() #dict_keys(['target', 'DESCR', 'data', 'target_names', 'feature_names'])#数据的条数和维数n_samples,n_features=iris.data.shapeprint("Number of sample:",n_samples) #Number of sample: 150print("Number of feature",n_features) #Number of feature 4#第⼀个样例print(iris.data[0]) #[ 5.1 3.5 1.4 0.2]print(iris.data.shape) #(150, 4)print(iris.target.shape) #(150,)print(iris.target)""" [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 12 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]"""import numpy as npprint(iris.target_names) #['setosa' 'versicolor' 'virginica']np.bincount(iris.target) #[50 50 50]import matplotlib.pyplot as plt#以第3个索引为划分依据，x_index的值可以为0，1，2，3x_index=3color=['blue','red','green']for label,color in zip(range(len(iris.target_names)),color):plt.hist(iris.data[iris.target==label,x_index],label=iris.target_names[label],color=color)plt.xlabel(iris.feature_names[x_index])plt.legend(loc="Upper right")plt.show()#画散点图，第⼀维的数据作为x轴和第⼆维的数据作为y轴x_index=0y_index=1colors=['blue','red','green']for label,color in zip(range(len(iris.target_names)),colors):plt.scatter(iris.data[iris.target==label,x_index],iris.data[iris.target==label,y_index],label=iris.target_names[label],c=color)plt.xlabel(iris.feature_names[x_index])plt.ylabel(iris.feature_names[y_index])plt.legend(loc='upper left')plt.show()⼿写数字数据集load_digits()：⽤于多分类任务的数据集from sklearn.datasets import load_digitsdigits=load_digits()print(digits.data.shape)import matplotlib.pyplot as pltplt.gray()plt.matshow(digits.images[0])plt.show()from sklearn.datasets import load_digitsdigits=load_digits()digits.keys()n_samples,n_features=digits.data.shapeprint((n_samples,n_features))print(digits.data.shape)print(digits.images.shape)import numpy as npprint(np.all(digits.images.reshape((1797,64))==digits.data))fig=plt.figure(figsize=(6,6))fig.subplots_adjust(left=0,right=1,bottom=0,top=1,hspace=0.05,wspace=0.05) #绘制数字：每张图像8*8像素点for i in range(64):ax=fig.add_subplot(8,8,i+1,xticks=[],yticks=[])ax.imshow(digits.images[i],cmap=plt.cm.binary,interpolation='nearest')#⽤⽬标值标记图像ax.text(0,7,str(digits.target[i]))plt.show()乳腺癌数据集load-barest-cancer（）：简单经典的⽤于⼆分类任务的数据集糖尿病数据集：load-diabetes（）：经典的⽤于回归认为的数据集，值得注意的是，这10个特征中的每个特征都已经被处理成0均值，⽅差归⼀化的特征值，波⼠顿房价数据集：load-boston（）：经典的⽤于回归任务的数据集体能训练数据集：load-linnerud（）：经典的⽤于多变量回归任务的数据集，其内部包含两个⼩数据集：Excise是对3个训练变量的20次观测（体重，腰围，脉搏），physiological是对3个⽣理学变量的20次观测（引体向上，仰卧起坐，⽴定跳远）svmlight/libsvm的每⼀⾏样本的存放格式：<label><feature-id>:<feature-value> <feature-id>:<feature-value> ....这种格式⽐较适合⽤来存放稀疏数据，在sklearn中，⽤scipy sparse CSR矩阵来存放X，⽤numpy数组来存放Yfrom sklearn.datasets import load_svmlight_filex_train,y_train=load_svmlight_file("/path/to/train_dataset.txt","")#如果要加在多个数据的时候，可以⽤逗号隔开②⽣成数据集⽣成数据集：可以⽤来分类任务，可以⽤来回归任务，可以⽤来聚类任务，⽤于流形学习的，⽤于因⼦分解任务的⽤于分类任务和聚类任务的：这些函数产⽣样本特征向量矩阵以及对应的类别标签集合make_blobs：多类单标签数据集，为每个类分配⼀个或多个正太分布的点集make_classification：多类单标签数据集，为每个类分配⼀个或多个正太分布的点集，提供了为数据添加噪声的⽅式，包括维度相关性，⽆效特征以及冗余特征等make_gaussian-quantiles：将⼀个单⾼斯分布的点集划分为两个数量均等的点集，作为两类make_hastie-10-2：产⽣⼀个相似的⼆元分类数据集，有10个维度make_circle和make_moom产⽣⼆维⼆元分类数据集来测试某些算法的性能，可以为数据集添加噪声，可以为⼆元分类器产⽣⼀些球形判决界⾯的数据#⽣成多类单标签数据集import numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets.samples_generator import make_blobscenter=[[1,1],[-1,-1],[1,-1]]cluster_std=0.3X,labels=make_blobs(n_samples=200,centers=center,n_features=2,cluster_std=cluster_std,random_state=0)print('X.shape',X.shape)print("labels",set(labels))unique_lables=set(labels)colors=plt.cm.Spectral(np.linspace(0,1,len(unique_lables)))for k,col in zip(unique_lables,colors):x_k=X[labels==k]plt.plot(x_k[:,0],x_k[:,1],'o',markerfacecolor=col,markeredgecolor="k",markersize=14)plt.title('data by make_blob()')plt.show()#⽣成⽤于分类的数据集from sklearn.datasets.samples_generator import make_classificationX,labels=make_classification(n_samples=200,n_features=2,n_redundant=0,n_informative=2,random_state=1,n_clusters_per_class=2)rng=np.random.RandomState(2)X+=2*rng.uniform(size=X.shape)unique_lables=set(labels)colors=plt.cm.Spectral(np.linspace(0,1,len(unique_lables)))for k,col in zip(unique_lables,colors):x_k=X[labels==k]plt.plot(x_k[:,0],x_k[:,1],'o',markerfacecolor=col,markeredgecolor="k",markersize=14)plt.title('data by make_classification()')plt.show()#⽣成球形判决界⾯的数据from sklearn.datasets.samples_generator import make_circlesX,labels=make_circles(n_samples=200,noise=0.2,factor=0.2,random_state=1) print("X.shape:",X.shape)print("labels:",set(labels))unique_lables=set(labels)colors=plt.cm.Spectral(np.linspace(0,1,len(unique_lables)))for k,col in zip(unique_lables,colors):x_k=X[labels==k]plt.plot(x_k[:,0],x_k[:,1],'o',markerfacecolor=col,markeredgecolor="k",markersize=14)plt.title('data by make_moons()')plt.show()。
非平衡数据集下的半监督迁移学习
非平衡数据集下的半监督迁移学习在机器学习领域，数据集的平衡性一直是一个重要的问题。
在实际应用中，我们经常会面对非平衡数据集，即某一类别的样本数量远远超过其他类别。
这种非平衡性会对机器学习算法的性能产生负面影响，因为算法更容易偏向数量较多的类别。
为了解决这个问题，研究者们提出了许多方法，其中之一就是半监督迁移学习。
半监督学习是指利用少量有标签样本和大量无标签样本进行训练的方法。
这种方法充分利用了无标签数据中蕴含的信息，并通过迁移学习将这些信息应用到目标领域中。
在非平衡数据集下进行半监督迁移学习可以更好地解决类别不平衡问题，并提高机器学习算法在非平衡数据集上的性能。
首先，我们需要了解为什么非平衡数据集会对机器学习算法产生负面影响。
在一个二分类问题中，如果某个类别只有很少数量的样本，在训练过程中算法可能会将其误分类为数量较多的类别。
这种情况下，模型将更加偏向数量较多的类别，导致对数量较少的类别的识别能力下降。
而半监督迁移学习可以通过利用无标签数据中的信息来解决这个问题。
半监督迁移学习的基本思想是通过将源领域中已标记样本的知识迁移到目标领域中，从而提高目标领域中样本分类的性能。
在非平衡数据集下，这种迁移可以更好地平衡不同类别之间的样本数量差异。
具体来说，我们可以利用源领域中大量无标签样本和少量有标签样本训练一个分类器，并将其应用到目标领域中。
在目标领域中，我们同样有大量无标签数据和少量有标签数据。
通过使用源领域训练好的分类器对目标领域进行预测，并将预测结果作为新的有标签数据加入到训练集中，我们可以不断地迭代这个过程来提高分类器在非平衡数据集上的性能。
半监督迁移学习在非平衡数据集下具有一些优势。
首先，它可以充分利用大量无标签样本中蕴含的信息来提高分类器性能。
在非平衡数据集中，无标签样本往往占据绝大部分，因此利用这些无标签样本可以更好地捕捉到数据集的整体分布情况。
其次，半监督迁移学习可以通过迁移源领域的知识来平衡不同类别之间的样本数量差异。
4.线性回归api与波士顿房价预测案例
4.线性回归api与波⼠顿房价预测案例线性回归api再介绍sklearn.linear_model.LinearRegression(fit_intercept=True)通过正规⽅程优化fit_intercept：是否计算偏置LinearRegression.coef_：回归系数LinearRegression.intercept_：偏置sklearn.linear_model.SGDRegressor(loss="squared_loss", fit_intercept=True, learning_rate ='invscaling', eta0=0.01) SGDRegressor类实现了随机梯度下降学习，它⽀持不同的loss函数和正则化惩罚项来拟合线性回归模型。
loss:损失类型loss=”squared_loss”: 普通最⼩⼆乘法fit_intercept：是否计算偏置learning_rate : string, optional学习率填充'constant': eta = eta0'optimal': eta = 1.0 / (alpha * (t + t0)) [default]'invscaling': eta = eta0 / pow(t, power_t)power_t=0.25:存在⽗类当中对于⼀个常数值的学习率来说，可以使⽤learning_rate=’constant’ ，并使⽤eta0来指定学习率。
SGDRegressor.coef_：回归系数SGDRegressor.intercept_：偏置 波⼠顿房价预测 1：数据集介绍给定的这些特征，是专家们得出的影响房价的结果属性。
我们此阶段不需要⾃⼰去探究特征是否有⽤，只需要使⽤这些特征。
到后⾯量化很多特征需要我们⾃⼰去寻找1 分析回归当中的数据⼤⼩不⼀致，是否会导致结果影响较⼤。
COMSOL使用技巧_V1.0_2013-02
COMSOL 使用技巧中仿科技公司CnTech Co.,Ltd目录一、1.11.21.31.41.51.6二、2.12.22.32.4三、3.13.23.33.43.5四、4.14.24.34.44.5五、5.15.25.3六、6.16.26.36.46.5七、几何建模................................................................................................................................. - 1 -组合体和装配体................................................................................................................. - 1 -隐藏部分几何..................................................................................................................... - 2 -工作面................................................................................................................................. - 3 -修整导入的几何结构......................................................................................................... - 4 -端盖面............................................................................................................................... - 11 -虚拟几何........................................................................................................................... - 12 -网格剖分............................................................................................................................... - 14 -交互式网格剖分............................................................................................................... - 14 -角细化............................................................................................................................... - 16 -自适应网格....................................................................................................................... - 16 -自动重新剖分网格........................................................................................................... - 18 -模型设定............................................................................................................................... - 19 -循序渐进地建模............................................................................................................... - 19 -开启物理符号................................................................................................................... - 19 -利用装配体....................................................................................................................... - 21 -调整方程形式................................................................................................................... - 22 -修改底层方程................................................................................................................... - 23 -求解器设定........................................................................................................................... - 25 -调整非线性求解器........................................................................................................... - 25 -确定瞬态求解的步长....................................................................................................... - 26 -停止条件........................................................................................................................... - 27 -边求解边绘图................................................................................................................... - 28 -绘制探针图....................................................................................................................... - 29 -弱约束的应用技巧............................................................................................................... - 31 -一个边界上多个约束....................................................................................................... - 31 -约束总量不变................................................................................................................... - 32 -自定义本构方程............................................................................................................... - 34 -后处理技巧........................................................................................................................... - 36 -组合图形........................................................................................................................... - 36 -显示内部结果................................................................................................................... - 37 -绘制变形图....................................................................................................................... - 38 -数据集组合....................................................................................................................... - 39 -导出数据........................................................................................................................... - 39 -函数使用技巧....................................................................................................................... - 43 -7.17.27.37.4八、8.18.2九、9.19.2十、10.110.210.310.4十一、11.111.211.311.411.511.6随机函数........................................................................................................................... - 43 -周期性函数....................................................................................................................... - 44 -高程函数........................................................................................................................... - 45 -内插函数........................................................................................................................... - 46 -耦合变量的使用技巧........................................................................................................... - 48 -积分耦合变量................................................................................................................... - 48 -拉伸耦合变量................................................................................................................... - 49 -ODE 的使用技巧................................................................................................................... - 50 -模拟不可逆形态变化....................................................................................................... - 50 -反向工程约束................................................................................................................... - 51 -MATLAB 实时链接................................................................................................................ - 52 -同时打开两种程序GUI................................................................................................. - 52 -在COMSOL 中使用MATLAB 脚本................................................................................ - 52 -在MATLAB 中编写GUI ................................................................................................. - 53 -常用脚本指令................................................................................................................ - 54 -其他................................................................................................................................... - 56 -局部坐标系.................................................................................................................... - 56 -应力集中问题................................................................................................................ - 56 -灵活应用案例库............................................................................................................ - 57 -经常看看在线帮助........................................................................................................ - 57 -临时文件........................................................................................................................ - 58 -物理场开发器................................................................................................................ - 59 -一、几何建模COMSOL Multiphysics 提供丰富的工具，供用户在图形化界面中构建自己的几何模型，例如1D 中通过点、线，2D 中可以通过点、线、矩形、圆/椭圆、贝塞尔曲线等，3D 中通过球/椭球、立方体、台、点、线等构建几何结构，另外，通过镜像、复制、移动、比例缩放等工具对几何对象进行高级操作，还可以通过布尔运算方式进行几何结构之间的切割、粘合等操作。
Spark大数据处理系列之Machine Learning
Spark大数据处理系列之Machine Learning超人学院——机器学习和数据科学机器学习是从已经存在的数据进行学习来对将来进行数据预测，它是基于输入数据集创建模型做数据驱动决策。
数据科学是从海里数据集（结构化和非结构化数据）中抽取知识，为商业团队提供数据洞察以及影响商业决策和路线图。
数据科学家的地位比以前用传统数值方法解决问题的人要重要。
以下是几类机器学习模型：∙监督学习模型∙非监督学习模型∙半监督学习模型∙增强学习模型下面简单的了解下各机器学习模型，并进行比较：∙监督学习模型：监督学习模型对已标记的训练数据集训练出结果，然后对未标记的数据集进行预测；监督学习又包含两个子模型：回归模型和分类模型。
∙非监督学习模型：非监督学习模型是用来从原始数据（无训练数据）中找到隐藏的模式或者关系，因而非监督学习模型是基于未标记数据集的；∙半监督学习模型：半监督学习模型用在监督和非监督机器学习中做预测分析，其既有标记数据又有未标记数据。
典型的场景是混合少量标记数据和大量未标记数据。
半监督学习一般使用分类和回归的机器学习方法；∙增强学习模型：增强学习模型通过不同的行为来寻找目标回报函数最大化。
下面给各个机器学习模型举个列子：∙监督学习：异常监测；∙非监督学习：社交网络，语言预测；∙半监督学习：图像分类、语音识别；∙增强学习：人工智能（AI）。
机器学习项目步骤开发机器学习项目时，数据预处理、清洗和分析的工作是非常重要的，与解决业务问题的实际的学习模型和算法一样重要。
典型的机器学习解决方案的一般步骤：∙特征工程∙模型训练∙模型评估图1原始数据如果不能清洗或者预处理，则会造成最终的结果不准确或者不可用，甚至丢失重要的细节。
训练数据的质量对最终的预测结果非常重要，如果训练数据不够随机，得出的结果模型不精确；如果数据量太小，机器学习出的模型也不准确。
使用案例：业务使用案例分布于各个领域，包括个性化推荐引擎（食品推荐引擎），数据预测分析（股价预测或者预测航班延迟），广告，异常监测，图像和视频模型识别，以及其他各类人工智能。
机器学习_ParkinsonsDataSet（帕金森数据集）
机器学习_ParkinsonsDataSet（帕⾦森数据集）Parkinsons Data Set(帕⾦森数据集)数据摘要：Oxford Parkinson's Disease Detection Dataset.This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD.中⽂关键词：帕⾦森,多变量,分类,UCI,英⽂关键词：Parkinsons,Multivariate,Classification,UCI,数据格式：TEXT数据⽤途：This data set is used for classification.数据详细介绍：Parkinsons Data SetAbstract: Oxford Parkinson's Disease Detection DatasetSource:The dataset was created by Max Little of the University of Oxford, in collaboration with the National Centre for Voice and Speech, Denver, Colorado, who recorded the speech signals. The original study published the feature extraction methods for general voice disorders.Data Set Information:This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD.The data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around six recordings per patient, the name of the patient is identified in the first column.For further information or to pass on comments, please contact Max Little (littlem '@' /doc/6e7149603.html).Further details are contained in the following reference -- if you use this dataset, please cite:Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2008), 'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', IEEE Transactions on Biomedical Engineering (to appear). Attribute Information: Matrix column entries (attributes):name - ASCII subject name and recording numberMDVP:Fo(Hz) - Average vocal fundamental frequencyMDVP:Fhi(Hz) - Maximum vocal fundamental frequencyMDVP:Flo(Hz) - Minimum vocal fundamental frequencyMDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP - Several measures of variation in fundamental frequencyMDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP: APQ,Shimmer:DDA - Several measures of variation in amplitudeNHR,HNR - Two measures of ratio of noise to tonal components in the voice status - Health status of the subject (one) -Parkinson's, (zero) - healthy RPDE,D2 - Two nonlinear dynamical complexity measuresDFA - Signal fractal scaling exponentspread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation数据预览：点此下载完整数据集。
机器学习_boston dataset(波士顿数据集)
boston dataset(波士顿数据集)数据摘要：A small but widely used dataset concerning housing in the Boston Massachusetts area. It has been adapted from the UCI repository of machine learning databases. More information is available in the detailed documentation.中文关键词：波士顿,数据集,房屋,机器学习,英文关键词：boston,dataset,housing,machine learning,数据格式：TEXT数据用途：Information ProcessingClassification数据详细介绍：boston datasetA Dataset derived from information collected by the U.S. Census Service concerning housing in the area of Boston Mass.This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive(/datasets/boston), and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases.The data was originally published by Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.Dataset NamingThe name for this dataset is simply boston. It has two prototasks: nox, in which the nitrous oxide level is to be predicted; and price, in which the median value of a home is to be predictedMiscellaneous DetailsOriginThe origin of the boston housing data is Natural.UsageThis dataset may be used for Assessment.Number of CasesThe dataset contains a total of 506 cases.OrderThe order of the cases is mysterious.VariablesThere are 14 attributes in each case of the dataset. They are:CRIM - per capita crime rate by townZN - proportion of residential land zoned for lots over 25,000 sq.ft.INDUS - proportion of non-retail business acres per town.CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)NOX - nitric oxides concentration (parts per 10 million)RM - average number of rooms per dwellingAGE - proportion of owner-occupied units built prior to 1940DIS - weighted distances to five Boston employment centresRAD - index of accessibility to radial highwaysTAX - full-value property-tax rate per $10,000PTRATIO - pupil-teacher ratio by townB - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by townLSTAT - % lower status of the populationMEDV - Median value of owner-occupied homes in $1000'sNoteVariable #14 seems to be censored at 50.00 (corresponding to a median price of $50,000); Censoring is suggested by the fact that the highest median price of exactly $50,000 is reported in 16 cases, while 15 cases have prices between $40,000 and $50,000, with prices rounded to the nearest hundred. Harrison and Rubinfeld do not mention any censoring.数据预览：点此下载完整数据集。
对Boston数据集的分析--统计机器学习期中考试知识分享
组员：郭晋 郭煜 田甜 刘一诺
Questions：
• 怎样预测波士顿的犯罪率？ • 怎么预测某市民是否犯罪？
Q1：怎样预测波士顿的犯罪率
• 下面是我们的解决过程： 1、做出每个变量对变量crim的简单线性回归，进行预测 2、由简单线性回归的预测，做残差分析，发现预测效果不佳 3、以crim为响应变量，其余变量为预测变量，做多元线性回归。 4、发现多元线性回归预测效果不佳，进行多元回归分析，不断改变归回模型，最终 得到最佳回归模型。
➢Multiple R-squared: 0.02611,
Adjusted R-squared: 0.0237
➢F-statistic: 10.83 on 1 and 404 DF, p-value: 0.001086
发现p值小于0.01，我们有理由认为zn和crim之间存在关联
之后，画出zn对crim的散点图与拟合曲线，我们发现，拟合效果不佳。 ➢ plot(Boston$zn,Boston$crim) ➢ abline(lm.fit0)
系数
-0.04657 0.40041 -0.355 24.447 -1.8314 0.07469 -1.1015 0.70423 0.032243 0.7263 -0.00853 0.43449 -0.25013
p值
0.00109 7.25E-14 0.783 8.20E-16 0.000192 9.08E-10 8.65E-12< 2e-16 <2e-16 6.71E-06
cooksdistance衡量的是一个某样本的改变会使得所有样本的残差改变的幅度该值越大说明该样本异常为使回归模型预测效果更好我们通过对预测变量做非线性变换来改进模型我们分别作了对数变换平方变换和开方变换发现对数变换预测性最好最优拟合是lmfit13对应的是多元回归拟合这个答案是合理的lmfit13对训练集的数据拟合程度最高也就导致了它预测性丌会比做了非线性变换之后的回归好
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boston dataset(波士顿数据集)
数据摘要：
A small but widely used dataset concerning housing in the Boston Massachusetts area. It has been adapted from the UCI repository of machine learning databases. More information is available in the detailed documentation.
中文关键词：
波士顿,数据集,房屋,机器学习,
英文关键词：
boston,dataset,housing,machine learning,
数据格式：
TEXT
数据用途：
Information Processing
Classification
数据详细介绍：
boston dataset
A Dataset derived from information collected by the U.S. Census Service concerning housing in the area of Boston Mass.
This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive
(/datasets/boston), and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases.
The data was originally published by Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.
Dataset Naming
The name for this dataset is simply boston. It has two prototasks: nox, in which the nitrous oxide level is to be predicted; and price, in which the median value of a home is to be predicted
Miscellaneous Details
Origin
The origin of the boston housing data is Natural.
Usage
This dataset may be used for Assessment.
Number of Cases
The dataset contains a total of 506 cases.
Order
The order of the cases is mysterious.
Variables
There are 14 attributes in each case of the dataset. They are:
CRIM - per capita crime rate by town
ZN - proportion of residential land zoned for lots over 25,000 sq.ft.
INDUS - proportion of non-retail business acres per town.
CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)
NOX - nitric oxides concentration (parts per 10 million)
RM - average number of rooms per dwelling
AGE - proportion of owner-occupied units built prior to 1940
DIS - weighted distances to five Boston employment centres
RAD - index of accessibility to radial highways
TAX - full-value property-tax rate per $10,000
PTRATIO - pupil-teacher ratio by town
B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
LSTAT - % lower status of the population
MEDV - Median value of owner-occupied homes in $1000's
Note
Variable #14 seems to be censored at 50.00 (corresponding to a median price of $50,000); Censoring is suggested by the fact that the highest median price of exactly $50,000 is reported in 16 cases, while 15 cases have prices between $40,000 and $50,000, with prices rounded to the nearest hundred. Harrison and Rubinfeld do not mention any censoring.
数据预览：
点此下载完整数据集。