Stay organized with collections Save and categorize content based on your preferences.
Estimated module length: 70 minutes Learning objectivesThis module assumes you are familiar with the concepts covered in the following modules:
In the Logistic regression module, you learned how to use the sigmoid function to convert raw model output to a value between 0 and 1 to make probabilistic predictions—for example, predicting that a given email has a 75% chance of being spam. But what if your goal is not to output probability but a category—for example, predicting whether a given email is "spam" or "not spam"?
Classification is the task of predicting which of a set of classes (categories) an example belongs to. In this module, you'll learn how to convert a logistic regression model that predicts a probability into a binary classification model that predicts one of two classes. You'll also learn how to choose and calculate appropriate metrics to evaluate the quality of a classification model's predictions. Finally, you'll get a brief introduction to multi-class classification problems, which are discussed in more depth later in the course.
Key terms:Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-10-16 UTC.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-10-16 UTC."],[[["This module focuses on converting logistic regression models into binary classification models for predicting categories instead of probabilities."],["You'll learn how to determine the optimal threshold for classification, calculate and select appropriate evaluation metrics, and interpret ROC and AUC."],["The module covers binary and provides an introduction to multi-class classification, building upon prior knowledge of machine learning, linear regression, and logistic regression."],["The content explores methods for evaluating the quality of classification model predictions and applying them to real-world scenarios."]]],[]]
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4