web数据挖掘课件CS583-supervised-learning
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A decision is needed: whether to put a new patient in an intensive-care unit.
Due to the high cost of ICU, those patients who may survive less than a month are given higher priority.
Supervision: The data (observations, measurements, etc.) are labeled with pre-defined classes. It is like that a “teacher” gives the classes (supervision).
CS583, Bing Liห้องสมุดไป่ตู้, UIC
2
An example application
An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients.
The task is commonly called: Supervised learning, classification, or inductive learning.
CS583, Bing Liu, UIC
5
The data and the goal
Data: A set of data records (also called examples, instances or cases) described by
web数据挖掘课件CS583-supervised-learning
Road Map
Basic concepts Decision tree induction Evaluation of classifiers Rule induction Classification using association rules Naïve Bayesian classification Naïve Bayes for text classification Support vector machines K-nearest neighbor Ensemble methods: Bagging and Boosting Summary
Our focus: learn a target function that can be used to predict the values of a discrete class attribute, e.g., approve or not-approved, and high-risk or low risk.
age Marital status annual salary outstanding debts credit rating etc.
Problem: to decide whether an application should approved, or to classify applications into two categories, approved and not approved.
k attributes: A1, A2, … Ak. a class: Each example is labelled with a pre-
defined class.
Goal: To learn a classification model from the data that can be used to predict the classes of new (future, or test) cases/instances.
CS583, Bing Liu, UIC
6
An example: data (loan application)
Approved or not
CS583, Bing Liu, UIC
7
An example: the learning task
Learn a classification model from the data Use the model to classify future loan applications
CS583, Bing Liu, UIC
4
Machine learning and our focus
Like human learning from past experiences.
A computer does not have “experiences”.
A computer system learns from data, which represent some “past experiences” of an application domain.
Problem: to predict high-risk patients and discriminate them from low-risk patients.
CS583, Bing Liu, UIC
3
Another application
A credit card company receives thousands of applications for new cards. Each application contains information about an applicant,
into
Yes (approved) and No (not approved)
What is the class for following case/instance?
CS583, Bing Liu, UIC
8
Supervised vs. unsupervised Learning
Supervised learning: classification is seen as supervised learning from examples.
Due to the high cost of ICU, those patients who may survive less than a month are given higher priority.
Supervision: The data (observations, measurements, etc.) are labeled with pre-defined classes. It is like that a “teacher” gives the classes (supervision).
CS583, Bing Liห้องสมุดไป่ตู้, UIC
2
An example application
An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients.
The task is commonly called: Supervised learning, classification, or inductive learning.
CS583, Bing Liu, UIC
5
The data and the goal
Data: A set of data records (also called examples, instances or cases) described by
web数据挖掘课件CS583-supervised-learning
Road Map
Basic concepts Decision tree induction Evaluation of classifiers Rule induction Classification using association rules Naïve Bayesian classification Naïve Bayes for text classification Support vector machines K-nearest neighbor Ensemble methods: Bagging and Boosting Summary
Our focus: learn a target function that can be used to predict the values of a discrete class attribute, e.g., approve or not-approved, and high-risk or low risk.
age Marital status annual salary outstanding debts credit rating etc.
Problem: to decide whether an application should approved, or to classify applications into two categories, approved and not approved.
k attributes: A1, A2, … Ak. a class: Each example is labelled with a pre-
defined class.
Goal: To learn a classification model from the data that can be used to predict the classes of new (future, or test) cases/instances.
CS583, Bing Liu, UIC
6
An example: data (loan application)
Approved or not
CS583, Bing Liu, UIC
7
An example: the learning task
Learn a classification model from the data Use the model to classify future loan applications
CS583, Bing Liu, UIC
4
Machine learning and our focus
Like human learning from past experiences.
A computer does not have “experiences”.
A computer system learns from data, which represent some “past experiences” of an application domain.
Problem: to predict high-risk patients and discriminate them from low-risk patients.
CS583, Bing Liu, UIC
3
Another application
A credit card company receives thousands of applications for new cards. Each application contains information about an applicant,
into
Yes (approved) and No (not approved)
What is the class for following case/instance?
CS583, Bing Liu, UIC
8
Supervised vs. unsupervised Learning
Supervised learning: classification is seen as supervised learning from examples.