Contents
How to use the caret package in preprocessing?
12.1.1More versatile tools for preprocessing data 12.1.2Using additional data to measure performance 12.2An Example 12.3Case Weights 13Using Your Own Model in train 13.1Introduction 13.2Illustrative Example 1: SVMs with Laplacian Kernels 13.3Model Components 13.3.1The parameters Element 13.3.2The gridElement 13.3.3The fitElement
What is preprocessign data with caret-R is my hammer?
Preprocessign Data with caret. Introduction. caret, short for _C_lassification _A_nd _RE_gression _T_raining, is a set of functions that streamline the process for creating predictive models. Its two chief benefits are a uniform interface and standardization of common tasks.
How does preprocess change column names in caret?
The preProcess class can apply this transformation by including “pca” in the method argument. Doing this will also force scaling of the predictors. Note that when PCA is requested, predict.preProcess changes the column names to PC1, PC2 and so on.
How to train a model in caret package?
7.0.13Gaussian Process 7.0.14Generalized Additive Model 7.0.15Generalized Linear Model 7.0.16Handle Missing Predictor Data 7.0.17Implicit Feature Selection 7.0.18Kernel Method 7.0.19L1 Regularization 7.0.20L2 Regularization 7.0.21Linear Classifier 7.0.22Linear Regression 7.0.23Logic Regression 7.0.24Logistic Regression 7.0.25Mixture Model
Why is caret’s procedure for cross validation with PCA wrong?
I.e. caret’s procedure for cross-validation with PCA (or in fact, with any rescaling/centering method) is “wrong” because the pre-processing of data is performed on the cross-validation set as well as the training set? And if so, how big an impact would this have on the results? I didn’t see the lecture, so I can’t comment on what was said.
How to train neural networks in caret package?
16.2Partial Least Squares Discriminant Analysis 16.3Bagged MARS and FDA 16.4Bagging 16.4.1The fitFunction 16.4.2The predFunction 16.4.3The aggregateFunction 16.5Model Averaged Neural Networks 16.6Neural Networks with a Principal Component Step 16.7Independent Component Regression 17Measuring Performance 17.1Measures for Regression