[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-07-27 (世界標準時間)。"],[[["\u003cp\u003eThis module introduces logistic regression, a model used to predict the probability of an outcome, unlike linear regression which predicts continuous numerical values.\u003c/p\u003e\n"],["\u003cp\u003eLogistic regression utilizes the sigmoid function to calculate probability and employs log loss as its loss function.\u003c/p\u003e\n"],["\u003cp\u003eRegularization is crucial when training logistic regression models to prevent overfitting and improve generalization.\u003c/p\u003e\n"],["\u003cp\u003eThe module covers the comparison between linear and logistic regression and explores use cases for logistic regression.\u003c/p\u003e\n"],["\u003cp\u003eFamiliarity with introductory machine learning and linear regression concepts is assumed for this 35-minute module.\u003c/p\u003e\n"]]],[],null,["| **Estimated module length:** 35 minutes\n| **Learning Objectives**\n|\n| - Identify use cases for performing logistic regression.\n| - Explain how logistic regression models use the sigmoid function to calculate probability.\n| - Compare linear regression and logistic regression.\n| - Explain why logistic regression uses log loss instead of squared loss.\n| - Explain the importance of regularization when training logistic regression models.\n| **Prerequisites:**\n|\n| This module assumes you are familiar with the concepts covered in the\n| following modules:\n|\n| - [Introduction to Machine Learning](/machine-learning/intro-to-ml)\n| - [Linear regression](/machine-learning/crash-course/linear-regression)\n\nIn the [Linear regression module](/machine-learning/crash-course/linear-regression),\nyou explored how to construct a model to make continuous numerical\npredictions, such as the fuel efficiency of a car. But what if you want to build\na model to answer questions like \"Will it rain today?\" or \"Is this email spam?\"\n\nThis module introduces a new type of regression model called\n[**logistic regression**](/machine-learning/glossary#logistic_regression)\nthat is designed to predict the probability of a given outcome. \n| **Key terms:**\n|\n- [Logistic regression](/machine-learning/glossary#logistic_regression) \n[Help Center](https://support.google.com/machinelearningeducation)"]]
