These HTML pages were created using bookdown. You can always email me with questions,comments or suggestions. DataCamp has a beginner’s tutorial on machine learning in R using caret.At useR! 2014, I was interviewed and discussed the package and the book.There is a webinar for the package on Youtube that was organized and recorded by Ray DiGiacomo Jr for the Orange County R User Group.The example data can be obtained here(the predictors) and here (the outcomes). There is also a paper on caret in the Journal of Statistical Software.It is on sale at Amazon or the the publisher’s website. The book Applied Predictive Modeling features caret and over 40 other R packages.The current release version can be found on CRAN and the project is hosted on github. 2021) (short for Classification And REgression Training) to carry out all our machine learning work in R. The package started off as a way to provide a uniform interface the functions themselves, as well as a way to standardize common tasks (such parameter tuning and variable importance). We will use the caret R package (Kuhn et al. Some have different syntax for model training and/or prediction. There are many different modeling functions in R. The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models. The caret package (short for Classification And REgression Training) contains functions to streamline the model training process for complex regression and. 22.2 Internal and External Performance Estimates.22 Feature Selection using Simulated Annealing.21.2 Internal and External Performance Estimates. 21 Feature Selection using Genetic Algorithms.20.3 Recursive Feature Elimination via caret.20.2 Resampling and External Validation.19 Feature Selection using Univariate Filters.18.1 Models with Built-In Feature Selection.16.6 Neural Networks with a Principal Component Step.16.2 Partial Least Squares Discriminant Analysis.16.1 Yet Another k-Nearest Neighbor Function.13.9 Illustrative Example 6: Offsets in Generalized Linear Models.13.8 Illustrative Example 5: Optimizing probability thresholds for class imbalances.13.7 Illustrative Example 4: PLS Feature Extraction Pre-Processing.13.6 Illustrative Example 3: Nonstandard Formulas.13.5 Illustrative Example 2: Something More Complicated - LogitBoost.13.2 Illustrative Example 1: SVMs with Laplacian Kernels.12.1.2 Using additional data to measure performance.12.1.1 More versatile tools for preprocessing data.11.4 Using Custom Subsampling Techniques.7.0.27 Multivariate Adaptive Regression Splines.5.9 Fitting Models Without Parameter Tuning.5.8 Exploring and Comparing Resampling Distributions.5.7 Extracting Predictions and Class Probabilities.5.1 Model Training and Parameter Tuning.4.4 Simple Splitting with Important Groups.4.1 Simple Splitting Based on the Outcome.3.2 Zero- and Near Zero-Variance Predictors.
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