Logistic Regression

Lesson 15 of 19 in Coddy's Introduction to Machine Learning course.

Logistic regression, despite its name, is a linear model used primarily for classification tasks, not for regression. It's ideal for situations where you want to predict the presence or absence of something, such as whether or not an email is spam. Logistic regression works well for binary classification but can be extended to multiclass classification through different techniques.

Source: wikipedia

At its core, logistic regression predicts the probability that a given input point belongs to a certain class. Unlike linear regression which predicts a continuous output, logistic regression predicts a binary outcome (1/0, Yes/No). The crux of logistic regression lies in the logistic function or sigmoid function, which maps any real-valued number into a value between 0 and 1, making it suitable for probability estimation.

Fitting a logistic regression model involves finding the best set of coefficients for the equation given the training data.

While it's efficient and straightforward for binary classification, its performance might lag behind more complex models for intricate datasets.

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

Challenge

Easy

Create a function named sigmoid that takes a list that represents a data point and returns the output of the sigmoid functions. Use numpy to solve this problem.

Try it yourself

import numpy as np
def sigmoid(point):
    arr = np.array(point)
    # Write code here

All lessons in Introduction to Machine Learning

1The Beginning of ML Journey

ML Introduction Data to feed a model What is a ML model

4K-Means Algorithm

K-Means Introduction fit method predict method

2Machine Learning Overview

Supervised learning Unsupervised learning

5Naive Bayes

Naive Bayes Introduction fit method predict method

3K-Nearest-Neighbors (KNN)

KNN Introduction fit method predict method

6Other Models

Logistic RegressionLinear Regression Decision Tree Support Vector Machine (SVM)Models quiz