![]() ![]() Most machine learning methods can be split into supervised or unsupervised categories. 6.3.1 \(C_p\), AIC, BIC, and Adjusted \(R^2\).Ģ.2.4 Supervised Versus Unsupervised Learning.6 Linear Model Selection And Regularization.5.1.5 Cross-Validation on Classification Problems. ![]() 5.1.4 Bias-Variance Trade-Off for k-fold Cross-Validation.4.7 Lab: Logistic Regression, LDA, QDA, and KNN.4.6 A Comparison of Classification Methods.4.5.3 Linear Discriminant Analysis for p > 1.4.5.2 Linear Discriminant Analysis for p = 1.4.5.1 Using Bayes’ Theorem for Classification.4.4.5 Logistic Regression for >2 Response Classes.4.4.2 Estimating the Regression Coefficients.3.4.4 Non-linear Transformations of the Predictors.3.3.8 Comparison of Linear Regression with K-Nearest Neighbors.3.3.4 Qualitative Predictors with More than Two Levels.3.3.3 Other Considerations in the Regression Model.3.3.1 Estimating the Regression Coefficients.3.2.3 Assessing the Accuracy of the Model.3.2.2 Assessing the Accuracy of the Coefficient Estimate.2.2.5 Regression Versus Classification Problems.2.2.4 Supervised Versus Unsupervised Learning.2.2.3 The Trade-Off Between Prediction Accuracy and Model Interpretability.1.2 Data Sets Used in Labs and Exercises.1.1 An Overview of Statistical Learning.A Tidy Introduction To Statistical Learning. ![]()
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