ISLR Notes
About
1
Introduction
1.1
An Overview of Statistical Learning
1.2
Data sets
1.2.1
Wages
1.2.2
Stock Market Data
1.2.3
Gene Expression Data
1.3
History
1.4
Other Considerations
1.5
Matrix Notation
2
Statistical Learning
2.1
2.1 What Is Statistical Learning?
2.1.1
2.1.1 Why Estimate f?
2.1.2
2.1.2 How Do We Estimate
\(f\)
?
2.1.3
2.1.3 The Trade-Off Between Prediction Accuracy and Model Interpretability
2.1.4
2.1.4 Supervised Versus Unsupervised Learning
2.1.5
2.1.5 Regression Versus Classification Problems
2.2
2.2 Assessing Model Accuracy
2.2.1
2.2.1 Measuring the Quality of Fit
2.2.2
2.2.2 The Bias-Variance Trade-Off
2.2.3
2.2.3 The Classification Setting
2.3
2.3 Lab: Introduction to R
2.3.1
2.3.1 Basic Commands
2.3.2
2.3.2 Graphics
2.3.3
2.3.3 Indexing Data
2.3.4
2.3.4 Loading Data
2.3.5
2.3.5 Additional Graphical and Numerical Summaries
2.4
2.4 Exercises
2.4.1
Conceptual
2.4.2
Applied
3
Linear Regression
3.1
Simple linear regression
3.1.1
Estimating Coefficients
3.1.2
Simulated Data Example
3.1.3
Closed-form solution
3.2
Assessing Accuracy of Coefficients
3.3
Assesing the Accuracy of the Model
4
Classification
5
Resampling Methods
6
Model Selection and Regularization
7
Moving Beyond Linearity
8
Tree Based Methods
9
Support Vector Machines
10
Unsupervised Learning
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ISLR Notes
Chapter 7
Moving Beyond Linearity