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In the Linear regression module,
you explored how to construct a model to make continuous numerical
predictions, such as the fuel efficiency of a car. But what if you want to build
a model to answer questions like "Will it rain today?" or "Is this email spam?"
This module introduces a new type of regression model called
logistic regression
that is designed to predict the probability of a given outcome.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-11-08 UTC."],[[["This module introduces logistic regression, a model used to predict the probability of an outcome, unlike linear regression which predicts continuous numerical values."],["Logistic regression utilizes the sigmoid function to calculate probability and employs log loss as its loss function."],["Regularization is crucial when training logistic regression models to prevent overfitting and improve generalization."],["The module covers the comparison between linear and logistic regression and explores use cases for logistic regression."],["Familiarity with introductory machine learning and linear regression concepts is assumed for this 35-minute module."]]],[]]