logit-1__logistic-regression-1.md

Logistic Regression Intro

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Objective

Learn Logistic Regression

Essentials Reading

Understanding Classifications

Read the basics of classifications

Basics of Logistic Regression

• Statistics PL16 - Logistic Regression - a video series by Brandon Foltz offer very good coverage of Logistic Regression. Start with the following videos:
  • Statistics 101: Logistic Regression, An Introduction
  • Statistics 101: Logistic Regression, Logit and Regression Equation
• Linear vs. Logistic Regression - video

Implementing Logistic Regression in Scikit-Learn

• Understanding Logistic Regression in Python
• Logistic Regression in Python

Extra Reading

• Logistic regression a quick intro
• Ch 4 - Classification of book Introduction to Statistical Learning offers good overview of Classification.
  • Section 4.3 covers Logistic Regression

Checklist

Check your knowledge:

• What is a sigmoid curve?
• What is the min/max values for sigmoid?
• How do we convert the sigmoid / logistic output to a binary classification (yes/no or true/false)
• Why is logistic called regression, but we use it for classification?

Exercises

Difficulty Level

★☆☆ - Easy
★★☆ - Medium
★★★ - Challenging
★★★★ - Bonus

EX-1: Practice logistic regression with synthetic data

Use Scikit's make_blobs or make_classification to generate some sample data.

Try to separate them using LogisticRegression

EX-2: College Admission Data

Load college admission data

It will look like this:

    admit  gre   gpa  rank
0       0  380  3.61     3
1       1  660  3.67     3
2       1  800  4.00     1
3       0  640  3.19     4
4       0  520  2.93     4
...

• Use input features: gre, gpa, rank, and predict output: admit
• Use Logistic Regression to predict.
• Create a confusion matrix
• What is the accuracy of the model

EX-3 - BONUS Lab

• Choose a classification dataset from UCI data repository
• For example, consider US Census Income data
• Run Logistic regression on the data of choice

More Exercises

• More logistc regression exercises

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