[[["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):2025-07-27。"],[[["\u003cp\u003eThis module focuses on converting logistic regression models into binary classification models for predicting categories instead of probabilities.\u003c/p\u003e\n"],["\u003cp\u003eYou'll learn how to determine the optimal threshold for classification, calculate and select appropriate evaluation metrics, and interpret ROC and AUC.\u003c/p\u003e\n"],["\u003cp\u003eThe module covers binary and provides an introduction to multi-class classification, building upon prior knowledge of machine learning, linear regression, and logistic regression.\u003c/p\u003e\n"],["\u003cp\u003eThe content explores methods for evaluating the quality of classification model predictions and applying them to real-world scenarios.\u003c/p\u003e\n"]]],[],null,["| **Estimated module length:** 70 minutes\n| **Learning objectives**\n|\n| - Determine an appropriate threshold for a binary classification model.\n| - Calculate and choose appropriate metrics to evaluate a binary classification model.\n| - Interpret ROC and AUC.\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| - [Logistic regression](/machine-learning/crash-course/logistic-regression)\n\nIn the [Logistic regression module](/machine-learning/crash-course/logistic-regression),\nyou learned how to use the [**sigmoid function**](/machine-learning/glossary#sigmoid-function)\nto convert raw model output to a value between 0 and 1 to make probabilistic\npredictions---for example, predicting that a given email has a 75% chance of\nbeing spam. But what if your goal is not to output probability but a\ncategory---for example, predicting whether a given email is \"spam\" or \"not spam\"?\n\n[**Classification**](/machine-learning/glossary#classification-model) is\nthe task of predicting which of a set of [**classes**](/machine-learning/glossary#class)\n(categories) an example belongs to. In this module, you'll learn how to convert\na logistic regression model that predicts a probability into a\n[**binary classification**](/machine-learning/glossary#binary-classification)\nmodel that predicts one of two classes. You'll also learn how to\nchoose and calculate appropriate metrics to evaluate the quality of a\nclassification model's predictions. Finally, you'll get a brief introduction to\n[**multi-class classification**](/machine-learning/glossary#multi-class)\nproblems, which are discussed in more depth later in the course.\n| **Key terms:**\n|\n| - [Binary classification](/machine-learning/glossary#binary-classification)\n| - [Class](/machine-learning/glossary#class)\n| - [Classification](/machine-learning/glossary#classification-model)\n| - [Multi-class classification](/machine-learning/glossary#multi-class)\n- [Sigmoid function](/machine-learning/glossary#sigmoid-function) \n[Help Center](https://support.google.com/machinelearningeducation)"]]