Koleksiyonlar ile düzeninizi koruyun İçeriği tercihlerinize göre kaydedin ve kategorilere ayırın.
Doğrusal regresyon modülünde, bir arabanın yakıt verimliliği gibi sürekli sayısal tahminlerde bulunmak için nasıl bir model oluşturulacağını keşfettiniz. Peki "Bugün yağmur yağacak mı?" veya "Bu e-posta spam mi?" gibi soruları yanıtlayacak bir model oluşturmak isterseniz ne olur?
Bu modülde, belirli bir sonucun olasılığını tahmin etmek için tasarlanmış mantıksal regresyon adlı yeni bir regresyon modeli türü tanıtılmaktadır.
[[["Anlaması kolay","easyToUnderstand","thumb-up"],["Sorunumu çözdü","solvedMyProblem","thumb-up"],["Diğer","otherUp","thumb-up"]],[["İhtiyacım olan bilgiler yok","missingTheInformationINeed","thumb-down"],["Çok karmaşık / çok fazla adım var","tooComplicatedTooManySteps","thumb-down"],["Güncel değil","outOfDate","thumb-down"],["Çeviri sorunu","translationIssue","thumb-down"],["Örnek veya kod sorunu","samplesCodeIssue","thumb-down"],["Diğer","otherDown","thumb-down"]],["Son güncelleme tarihi: 2025-07-27 UTC."],[[["\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)"]]
