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Mar 27, 2019 · The caret package (short for C lassification A nd RE gression T raining) is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for: data splitting. pre-processing.
Mar 11, 2018 · Using caret package, you can build all sorts of machine learning models. In this tutorial, I explain the core features of the caret package and walk you through the step-by-step process of building predictive models. Be it logistic reg or adaboost, caret helps to find the optimal model in the shortest possible time.
- Selva Prabhakaran
- Value
- Description
- F_meas.table
- true
- "AUC"
- = NULL model.matrix
- true
- TRUE model.matrix
- FUN
- true
- NA
- Tools for Models Available in
- NA
- Description
- Arguments
- Details
- Author(s)
- Description
- Arguments
- BoxCoxTrans
- FUN
- NULL
- B+1 B
- B P P
- Value
- Source
- NULL
- NA
- NULL
- B+1 B
- Value
- J K L
- true
- x recipe
- NA
- B M M
- AdaBoost Classification Trees ( )
- Adaptive Mixture Discriminant Analysis ( )
- Adaptive-Network-Based Fuzzy Inference System ( )
- Adjacent Categories Probability Model for Ordinal Data (
- Bagged AdaBoost ( )
- Bagged Flexible Discriminant Analysis ( )
- Bagged MARS ( )
- Bagged MARS using gCV Pruning ( )
- Bayesian Additive Regression Trees ( )
- Bayesian Generalized Linear Model ( )
- Bayesian Regularized Neural Networks ( )
- Bayesian Ridge Regression ( )
- Bayesian Ridge Regression (Model Averaged) ( )
- Binary Discriminant Analysis ( )
- Boosted Generalized Additive Model ( )
- Boosted Generalized Linear Model ( )
- Boosted Linear Model ( )
- Boosted Tree ( )
- CART ( )
- CART or Ordinal Responses ( )
- Conditional Inference Random Forest ( )
- Cost-Sensitive CART ( )
- Cubist ( )
- Cumulative Probability Model for Ordinal Data ( )
- DeepBoost ( )
- Distance Weighted Discrimination with Polynomial Kernel ( )
- Distance Weighted Discrimination with Radial Basis Function Kernel ( )
- Elasticnet ( )
- Extreme Learning Machine ( )
- Factor-Based Linear Discriminant Analysis ( )
- Flexible Discriminant Analysis ( )
- Fuzzy Inference Rules by Descent Method ( )
- Fuzzy Rules Using Chi’s Method ( )
- Fuzzy Rules Using Genetic Cooperative-Competitive Learning and Pittsburgh (
- Fuzzy Rules via MOGUL ( )
- Fuzzy Rules via Thrift ( )
- Fuzzy Rules with Weight Factor ( )
- Gaussian Process ( )
- Gaussian Process with Radial Basis Function Kernel ( )
- Generalized Additive Model using Splines ( )
- Generalized Linear Model ( )
- Generalized Linear Model with Stepwise Feature Selection ( )
- Genetic Lateral Tuning and Rule Selection of Linguistic Fuzzy Systems ( )
- Greedy Prototype Selection ( )
- Heteroscedastic Discriminant Analysis ( )
- High Dimensional Discriminant Analysis ( )
- High-Dimensional Regularized Discriminant Analysis (
- Independent Component Regression ( )
- k-Nearest Neighbors ( )
- Learning Vector Quantization ( )
- Least Squares Support Vector Machine ( )
- Least Squares Support Vector Machine with Polynomial Kernel ( )
- Least Squares Support Vector Machine with Radial Basis Function Kernel (
- Linear Discriminant Analysis ( )
- Linear Distance Weighted Discrimination ( )
- Linear Regression ( )
- Linear Regression with Forward Selection ( )
- Localized Linear Discriminant Analysis ( )
- Logic Regression ( )
- Maximum Uncertainty Linear Discriminant Analysis ( )
- Model Averaged Naive Bayes Classifier ( )
- Model Averaged Neural Network ( )
- Multi-Layer Perceptron ( )
- Multilayer Perceptron Network with Dropout ( )
- Multilayer Perceptron Network with Weight Decay ( )
- Multivariate Adaptive Regression Spline ( )
- Multivariate Adaptive Regression Splines ( )
- Naive Bayes ( )
- Naive Bayes Classifier ( )
- Naive Bayes Classifier with Attribute Weighting ( )
- Nearest Shrunken Centroids ( )
- Negative Binomial Generalized Linear Model ( )
- Neural Networks with Feature Extraction ( )
- Non-Informative Model ( )
- Non-Negative Least Squares ( )
- Oblique Random Forest ( )
- Optimal Weighted Nearest Neighbor Classifier ( )
- Partial Least Squares ( )
- Partial Least Squares Generalized Linear Models ( )
- Patient Rule Induction Method ( )
- Penalized Discriminant Analysis ( )
- Penalized Multinomial Regression ( )
- Polynomial Kernel Regularized Least Squares ( )
- Principal Component Analysis ( )
- Projection Pursuit Regression ( )
- Quadratic Discriminant Analysis ( )
- Quadratic Discriminant Analysis with Stepwise Feature Selection (
- Quantile Random Forest ( )
- Radial Basis Function Kernel Regularized Least Squares ( )
- Radial Basis Function Network ( )
- Random Ferns ( )
- Random Forest Rule-Based Model ( )
- Regularized Discriminant Analysis ( )
- Regularized Linear Discriminant Analysis ( )
- Regularized Logistic Regression ( )
- Regularized Random Forest ( )
- Relaxed Lasso ( )
- Relevance Vector Machines with Linear Kernel ( )
- Relevance Vector Machines with Polynomial Kernel ( )
- Relevance Vector Machines with Radial Basis Function Kernel ( )
- Ridge Regression ( )
- Robust Linear Discriminant Analysis ( )
- Robust Linear Model ( )
- Robust Mixture Discriminant Analysis ( )
- Robust Quadratic Discriminant Analysis ( )
- Robust Regularized Linear Discriminant Analysis ( )
- Robust SIMCA ( )
- ROC-Based Classifier ( )
- Rotation Forest ( )
- Rule-Based Classifier ( )
- Semi-Naive Structure Learner Wrapper ( )
- Shrinkage Discriminant Analysis ( )
- SIMCA ( )
- Sparse Linear Discriminant Analysis ( )
- Sparse Partial Least Squares ( )
- Stochastic Gradient Boosting ( )
- Subtractive Clustering and Fuzzy c-Means Rules ( )
- Supervised Principal Component Analysis ( )
- Support Vector Machines with Boundrange String Kernel ( )
- Support Vector Machines with Class Weights ( )
- Support Vector Machines with Exponential String Kernel ( )
- Support Vector Machines with Polynomial Kernel ( )
- Support Vector Machines with Radial Basis Function Kernel ( )
- Support Vector Machines with Radial Basis Function Kernel (
- Support Vector Machines with Spectrum String Kernel ( )
- The Bayesian lasso ( )
- The lasso ( )
- Tree Augmented Naive Bayes Classifier ( )
- Tree Augmented Naive Bayes Classifier Structure Learner Wrapper (
- Tree Augmented Naive Bayes Classifier with Attribute Weighting (
- Tree Models from Genetic Algorithms ( )
- Variational Bayesian Multinomial Probit Regression ( )
- Wang and Mendel Fuzzy Rules ( )
- NA
- NULL
bbbDescr data frame of chemical descriptors logBBB vector of assay results
These classes can be used to estimate transformations and apply them to existing and future data
character vector of dimnames for the table numeric value or matrix for the rate of the "positive" class of the data. When has two levels, should be a single numeric value. Otherwise, data prevalence it should be a vector of numeric values with elements for each class. The vector should have names corresponding to the classes. mode a single characte...
rows equal to floor(p * length(y)) and columns. times for numeric , the number of breaks in the quantiles (see below) y an integer for the number of folds. a logical. When true, the values returned are the sample positions corresponding to the data used during training. This argument only works in conjunction with initialWindow horizon list = TRUE ...
integer, how many (if any) resamples to skip to thin the total amount a vector of groups whose length matches the number of rows in the overall data set. For bootstrap samples, simple random sampling is used. For other data splitting, the random sampling is done within the levels of when is a factor in an y y attempt to balance the class distributi...
If assumes that the first level of the factor variables corresponds to a relevant result but the lev argument can be used to change this. computes some overall measures of for performance (e.g. overall accuracy multiClassSummary and the Kappa statistic) and several averages of statistics calculated from "one-versus-all" configu-rations. For example...
Details section) A logical; means to completely remove the variable names from the col-
umn names fullRank A logical; should a full rank or less than full rank parameterization be used? If , factors are encoded to be consistent with and the resulting
there are no linear dependencies induced between the columns. factor vector. object newdata na.action n contrasts sparse drop2nd An object of class dummyVars data frame with the required columns function determining what should be done with missing values in . newdata The default is to predict .
These functions are wrappers for the specific prediction functions in each modeling package. In each case, the optimal tuning values given in the slot of the object are used tuneValue finalModel to predict. To get simple predictions for a new data set, the function can be used. Limits can be predict imposed on the range of predictions. See for more...
models using grid search. ... only: specifications to be passed to , , (for plot levelplot xyplot stripplot line plots). The function automatically sets some arguments (e.g. axis labels) but passing in values here will over-ride the defaults x an object of class . train digits an integer specifying the number of significant digits used to label the...
is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. matrix or data frame of values for examples. x matrix or da...
train These function show information about models and packages that are accessible via train modelLookup(model = NULL) checkInstall(pkg) getModelInfo(model = NULL, regex = TRUE, ...) model a character string associated with the argument of . If no value is method train passed, all models are returned. For , regular expressions can be getModelInfo ...
conducted against the whole name of the model. ... options to pass to grepl is good for getting information related to the tuning parameters for a model. modelLookup getModelInfo will return all the functions and metadata associated with a model. Both of these functions will only search within the models bundled in this package. will check to see i...
is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) contrasts a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. y object newdata matrix or data frame of target v...
Performs a principal components analysis on an object of class and returns the results resamples as an object with classes and . prcomp.resamples prcomp ## S3 method for class 'resamples' prcomp(x, metric = x$metrics[1], ...) ## S3 method for class 'prcomp.resamples' plot(x, what = "scree", dims = max(2, ncol(x$rotation)), ...) x metric ... what Fo...
object newdata object of class . knnreg a data frame or matrix of new observations. ... additional arguments.
This function is a method for the generic function for class . For the details see predict knnreg . This is essentially a copy of . knnreg predict.ipredknn
Max Kuhn, Chris Keefer, adapted from and knn predict.ipredknn predictors List predictors used in the model
This class uses a model fit to determine which predictors were used in the final model.
model object, list or terms ... not currently used For , , , , , , , , , , randomForestcforest ctree rpartipredbagg baggingearth fda pamr.trainsuperpc.train and , an attempt was made to report the predictors that were actually used in the bagEarth bagFDA final model. The predictors function can be called on the model object (as opposed to the train...
a vector of means (if centering was requested) std rotation vector of standard deviations (if scaling or PCA was requested) matrix of eigenvectors if PCA was requested method the value of method thresh ranges numComp the value of thresh a matrix of min and max values for each predictor when includes "range" method (and NULL otherwise) the number of...
object a list of two or more objects of class , or with a common set of train sbf rfe resampling indices in the object. For , it is an object control sort.resamples generated by . resamples only used for and and captures arguments to pass to sort modelCor or sort
will set the seeds using a random set of integers. Alternatively, a list can be used. The list should have elements where is the number of resamples. The first
elements of the list should be vectors of integers of length where is the
number of subsets being evaluated (including the full set). The last element of the list only needs to be a single integer (for the final model). See the Examples section below. if a parallel backend is loaded and available, should the function use it? More details on this function can be found at http://topepo.github.io/caret/recursive-feature-eli...
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caret: Classification and Regression Training. Misc functions for training and plotting classification and regression models. Version: 6.0-94. Depends: ggplot2, lattice (≥ 0.20), R (≥ 3.2.0) Imports: e1071, foreach, grDevices, methods, ModelMetrics (≥ 1.2.2.2), nlme, plyr, pROC, recipes (≥ 0.1.10), reshape2, stats, stats4, utils, withr ...
Aug 22, 2019 · Caret is a package in R created and maintained by Max Kuhn form Pfizer. Development started in 2005 and was later made open source and uploaded to CRAN. Caret is actually an acronym which stands for Classification And REgression Training (CARET).
People also ask
What is caret & how does it work?
What is a caret package in R?
What is caret in R?
How does Max Kuhn demonstrate caret?
Jun 24, 2020 · The caret package is the definitive guide to caret by Max Kuhn (main package author). It includes the list of over 230 models available in caret. Talk slides by Max Kuhn: These slides are from 2013 so some of the material is outdated, but this is a good overview of the package.
