learn-co-students / dsc-logistic-regression-intro-teacher-onboarding

Logistic Regression - Introduction

Introduction

In this you'll be introduced to a new type of machine learning technique: classification! You'll learn about an algorithm called logistic regression as well as different ways that data scientists can evaluate the performance of classification models.

Logistic Regression

You're familiar with linear regression to predict continuous values. You're now going to return to regression to look at how it can be used as a classifier instead to determine the likelihood of a given data point being associated with one of two categories.

We'll start by introducing the sigmoid function and showing how it can be used to fit a curve that matches a binary classifier (e.g. does someone make over or under $40k a year or are they a good or bad credit risk).

Evaluating Classifiers

We'll then look at the practicalities of evaluating logistic regression models based on precision, recall, and accuracy to evaluate other classifiers.

We also take a little time to look at how to plot a confusion matrix for a logistic regression classifier and introduce a couple of key concepts for determining the optimal precision-recall trade-off for a given classifier - Receiver Operating Characteristic (ROC) curves and AUC (the Area Under the Curve).

Class Imbalance Problems

We then introduce the concept of class imbalance. Imagine a classifier for cancer where only 1 screened individual in 1000 is sick. You could obtain over 99 percent accuracy by just saying everyone is fine, but that wouldn't be a very useful approach. We look at the ideas of class weights and over/undersampling and how they can be used to work with highly imbalanced classes.

Summary

It's important to be aware of logistic regression as one of the most basic classifiers that you can use, and many of the concepts around model evaluation will be useful whenever you're trying to solve a classification problem.