ISLR Chapter 1 — Introduction to Statistical Learning
Tags: datascience, machinelearning, tutorial
Published: 2020-11-08 23:25:20

> This is a summary of chapter 1 of the _Introduction to Statistical Learning_ textbook. I’ve written a 10-part guide that covers the entire book. The guide can be read at [my website](https://www.bijenpatel.com/guide/), or [here](https://dev.to/bijenpatel/series/9611) at DEV. Subscribe to stay up to date on my latest Data Science & Engineering guides!

# Overview of Statistical Learning

Statistical learning simply refers to the broad set of tools that are available for understanding data. There are two main types of statistical learning: supervised and unsupervised.

## Supervised Learning

Supervised learning involves building statistical models to predict outputs (_Y_) from inputs (_X_). For example, assume that we have a salary dataset for statisticians. The dataset consists of the experience level and salary for 10 different statisticians.


![statistician-salary-table.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1604918350275/11UlenfRo.png)

We could build a simple linear regre<span id="rmm">s</span>sion model to predict the salary of statisticians by using experience level as a predictor. This is an example of supervised learning, where we have supervising outputs (salary values) that guide us in developing a statistical model to determine the relationship between experience level and salary.

![statistician-salary-regression.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1604918368060/ThX7WZ9u8.png)

In general, there are two main types of supervised learning: regression and classification.

## **Regression**

Predicting a quantitative output is known as a regression problem. For example, predicting someone’s salary is a regression problem.

## **Classification**

Predicting a qualitative output is known as a classification problem. For example, predicting whether a stock will go up or down is a classification problem.

# Unsupervised Learning

Unsupervised learning involves building statistical models to determine relationships from inputs (_X_). There are no supervising outputs. For example, assume that we have a customer dataset. The dataset consists of the annual salary and annual spend on Amazon for 10 different individuals.

![amazon-salary-spend-table.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1604873951166/ZQcYFmRa2.png)

We could use a statistical clustering algorithm to group customers by their purchasing behavior. This is an example of unsupervised learning, where we **do not** have supervising outputs that already inform us which customers are low spenders, average spenders, or high spenders. Instead, we have to come up with the determination ourselves.

![amazon-clustering.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1604918399263/diwbeTAVx.png)

In general, there are two main types of unsupervised learning: clustering and association.

## Clustering

Determining groupings is known as a clustering problem. For example, grouping customers together based on purchasing behavior is a clustering problem.

## Association

Determining rules that describe large portions of a dataset is known as an association problem. For example, determining that people who buy _X_ also buy _Y_ is an association problem. A modern real-world example of this is Amazon’s “frequently bought together” product recommendations.

_Originally published at_ [https://www.bijenpatel.com](https://www.bijenpatel.com/guide/islr/introduction-to-statistical-learning/) _on August 1, 2020._