![]() ![]() embarked: port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton).parch number of parents / children aboard the Titanic.sibsp: number of siblings / spouses aboard the Titanic.pclass: ticket class (1 = 1st, 2 = 2nd, 3 = 3rd).In particular the following variables are available: The data contained in the files titanic_tr.csv (for training) and titanic_te.csv (and testing) are about the Titanic disaster (the files are available in the e-learning). For a simpler interpretation, in the odds scale, we can take the exponential transformation of the parameter: Consider for example the parameter of the Pregnancies variable which is positive (i.e. higher risk of diabetes) and equal to 0.1193692: this means that for a one-unit increase in the number of pregnancies, the log-odds increases by 0.1193692. The summary contains the parameter estimates and the corresponding p-values of the test checking \(H_0:\beta =0\) vs \(H_1: \beta\neq 0\). means that all the covariates but Outcome are included as regressors (this avoids to write the formula in the standard way: Outcome ~ Pregnancies+Glucose+.). # Residual deviance: 519.01 on 528 degrees of freedom # Null deviance: 703.68 on 536 degrees of freedom # (Dispersion parameter for binomial family taken to be 1) , data = tr, family = "binomial") summary(logreg) # 8.2 Linear regression model with step functions.7.4 SVM with more than 2 categories and more than 2 regressors.6.3 Gradient boosting: parameter tuning.6.1 Linear model, bagging and random forest.5.5 Model comparison by using the ROC curves.5.1 Credit data and classification tree.4.6 Changing the number of bagged trees.4.2 Another method for creating the training and testing set.3.7 Construction and plotting of the ROC curve.3.6 Classifiers comparison in terms of performance indexes.3.5 Linear and quadratic discriminant analysis.3.3 A new method for creating the training and testing set.3.2 Definition of a function for computing performance indexes.2.2.2 KNN classification with different values of \(k\).2.1.5 Comparison of KNN with the multiple linear model with quadratic terms. ![]() ![]() 2.1.4 Comparison of KNN with the multiple linear model.2.1.3 Implementation of KNN regression with different values of \(k\).2.1.2 Implementation of KNN regression with \(k=1\).2.1.1 Creation of the training and testing set: method 1. ![]()
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