An Introduction of Statistical Learning

Introduction

Statistical Learning

Linear Learning

• Linear regression is a very simple approach for supervised learning.
• Linear regression is a useful tool for pre- dicting a quantitative response.
• Many fancy statistical learning approaches can be seen as gener- alizations or extensions of linear regression.
• Least squares approach that is most commonly used to fit this model.

  Simple Linear Regression

  Simple linear regression lives up to its name: it is a very straightforward approach for predicting a quantitative response Y on the basis of a single predictor variable X.

Mathematically, we can write this linear relationship as

Y ≈ β0 + β1X.
It assumes that there is approximately a linear relationship between X and Y.
Notes : You might read “≈” as “is approximately modeled as”.
Description : We are regressing Y on X (or Y onto X).

Eg : sales ≈ β0 +β1 ×TV.

• We can regress sales onto TV by fitting the model.
• β0 and β1 are two unknown constants that represent the intercept and slope terms in the linear model.
• β0 and β1 are known as the model coefficients or parameters.

Multiple Linear Regression

Classification

Resampling Methods

Linear Model Selection and Regularization

Moving Beyond Linearity

Tree Based Methods

Support Vector Machines

Unsupervised Learning