[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2024-11-08。"],[[["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."]]],[]]
