/dports/devel/R-cran-caret/caret/tests/testthat/ |
H A D | trim_rpart.R | 10 class_trim <- train(Class ~ ., data = tr_dat, 11 method = "rpart", 18 class_notrim <- train(Class ~ ., data = tr_dat, 19 method = "rpart", 41 reg_trim <- train(y ~ ., data = tr_dat, 42 method = "rpart", 48 reg_notrim <- train(y ~ ., data = tr_dat, 66 class_trim <- train(Class ~ ., data = tr_dat, 74 class_notrim <- train(Class ~ ., data = tr_dat, 97 reg_trim <- train(y ~ ., data = tr_dat, [all …]
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H A D | test_ptypes.R | 11 none <- trainControl(method = "none") 13 f_plain <- train(mpg ~ ., data = mtcars, method = "lm", trControl = none) 14 f_oned <- train(mpg ~ wt, data = mtcars, method = "lm", trControl = none) 15 f_inter <- train(mpg ~ (.)^2, data = mtcars, method = "lm", trControl = none) globalVar 16 f_dmmy <- train(price ~ ., data = Sacramento, method = "lm", trControl = none) 18 xy_plain <- train(x = mtcars[, -1], y = mtcars$mpg, method = "lm", trControl = none) 19 xy_oned <- train(x = mtcars[, "wt", drop = FALSE], y = mtcars$mpg, method = "lm", trControl = none) 20 xy_dmmy <- train(x = Sacramento[, -7], y = Sacramento$price, method = "lm", trControl = none)
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H A D | trim_train.R | 10 class_trim <- train(Class ~ ., data = tr_dat, 11 method = "rpart", 20 class_notrim <- train(Class ~ ., data = tr_dat, 21 method = "rpart", 44 reg_trim <- train(y ~ ., data = tr_dat, 45 method = "rpart", 52 reg_notrim <- train(y ~ ., data = tr_dat, 70 class_trim <- train(Class ~ ., data = tr_dat, 79 class_notrim <- train(Class ~ ., data = tr_dat, 102 reg_trim <- train(y ~ ., data = tr_dat, [all …]
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H A D | trim_C5.R | 13 class_trim <- train(Class ~ ., data = tr_dat, 14 method = "C5.0", 21 class_notrim <- train(Class ~ ., data = tr_dat, 22 method = "C5.0", 47 class_trim <- train(Class ~ ., data = tr_dat, 48 method = "C5.0", 55 class_notrim <- train(Class ~ ., data = tr_dat, 81 class_trim <- train(Class ~ ., data = tr_dat, 89 class_notrim <- train(Class ~ ., data = tr_dat, 115 class_trim <- train(Class ~ ., data = tr_dat, [all …]
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H A D | trim_glm.R | 10 class_trim <- train(Class ~ ., data = tr_dat, 11 method = "glm", 13 trControl = trainControl(method = "none", 18 class_notrim <- train(Class ~ ., data = tr_dat, 19 method = "glm", 21 trControl = trainControl(method = "none", 41 reg_trim <- train(y ~ ., data = tr_dat, 42 method = "glm", 44 trControl = trainControl(method = "none", 48 reg_notrim <- train(y ~ ., data = tr_dat, [all …]
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H A D | trim_bayesglm.R | 10 class_trim <- train(Class ~ ., data = tr_dat, 11 method = "bayesglm", 13 trControl = trainControl(method = "none", 18 class_notrim <- train(Class ~ ., data = tr_dat, 19 method = "bayesglm", 21 trControl = trainControl(method = "none", 41 reg_trim <- train(y ~ ., data = tr_dat, 42 method = "bayesglm", 44 trControl = trainControl(method = "none", 48 reg_notrim <- train(y ~ ., data = tr_dat, [all …]
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H A D | trim_treebag.R | 10 class_trim <- train(Class ~ ., data = tr_dat, 11 method = "treebag", 13 trControl = trainControl(method = "none", 18 class_notrim <- train(Class ~ ., data = tr_dat, 19 method = "treebag", 21 trControl = trainControl(method = "none", 41 reg_trim <- train(y ~ ., data = tr_dat, 42 method = "treebag", 44 trControl = trainControl(method = "none", 48 reg_notrim <- train(y ~ ., data = tr_dat, [all …]
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H A D | trim_glmnet.R | 10 class_trim <- train(Class ~ ., data = tr_dat, 11 method = "glmnet", 13 trControl = trainControl(method = "none", 18 class_notrim <- train(Class ~ ., data = tr_dat, 19 method = "glmnet", 21 trControl = trainControl(method = "none", 41 reg_trim <- train(y ~ ., data = tr_dat, 42 method = "glmnet", 44 trControl = trainControl(method = "none", 48 reg_notrim <- train(y ~ ., data = tr_dat, [all …]
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H A D | test_minimal.R | 12 expect_error(train(bbbDescr, logBBB, 13 method = "earth", 15 trControl = trainControl(method = "none"))) 17 expect_error(train(bbbDescr, logBBB, 18 method = "earth", 20 trControl = trainControl(method = "none"))) 22 expect_error(train(mpg ~ cyl + disp, data = mtcars, 23 method = "gam", 25 trControl = trainControl(method = "none")))
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H A D | test_bad_class_options.R | 9 train(Class ~ ., data = train_dat, method = "rpart", 20 train(Class ~ ., data = train_dat, method = "rpart", 32 train(Class ~ ., data = train_dat, method = "rpart") 40 train(x = fattyAcids, y = oilType, method = "rpart",
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H A D | test_tibble.R | 14 ctrl <- trainControl(method = "repeatedcv", 21 train( 24 method = "glm", 36 train( 39 method = "glm", 50 train( 53 method = "glm",
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/dports/devel/R-cran-caret/caret/man/ |
H A D | train.Rd | 3 \name{train} 4 \alias{train} 5 \alias{train.default} 7 \alias{train.recipe} 10 train(x, ...) 12 \method{train}{default}( 15 method = "rf", 26 \method{train}{formula}(form, data, ..., weights, subset, na.action = na.fail, contrasts = NULL) 28 \method{train}{recipe}( 31 method = "rf", [all …]
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H A D | print.train.Rd | 2 % Please edit documentation in R/print.train.R 3 \name{print.train} 4 \alias{print.train} 5 \title{Print Method for the train Class} 7 \method{print}{train}( 17 \item{x}{an object of class \code{\link{train}}.} 38 Print the results of a \code{\link{train}} object. 54 rdaFit <- train(TrainData, TrainClasses, method = "rda", 55 control = trainControl(method = "cv")) 62 \code{\link{train}}
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H A D | predict.train.Rd | 3 % R/predict.train.R 7 \alias{predict.train} 29 \method{predict}{train}(object, newdata = NULL, type = "raw", na.action = na.omit, ...) 48 \item{object}{For \code{predict.train}, an object of class 50 \code{\link{train}}.} 60 \item{na.action}{the method for handling missing data} 71 \code{predict.train}. 113 knnFit <- train(Species ~ ., data = iris, method = "knn", 114 trControl = trainControl(method = "cv")) 116 rdaFit <- train(Species ~ ., data = iris, method = "rda", [all …]
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H A D | plot.train.Rd | 2 % Please edit documentation in R/ggplot.R, R/plot.train.R 3 \name{ggplot.train} 4 \alias{ggplot.train} 5 \alias{plot.train} 6 \title{Plot Method for the train Class} 8 \method{ggplot}{train}( 20 \method{plot}{train}( 34 \pkg{ggplot2} generic method} 94 rdaFit <- train(Species ~ ., 96 method = "rda", [all …]
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H A D | histogram.train.Rd | 2 % Please edit documentation in R/lattice.train.R 3 \name{histogram.train} 4 \alias{histogram.train} 5 \alias{stripplot.train} 6 \alias{xyplot.train} 7 \alias{densityplot.train} 10 \method{histogram}{train}(x, data = NULL, metric = x$metric, ...) 13 \item{x}{An object produced by \code{\link{train}}} 40 produced (see the \code{method} argument of \code{\link{trainControl}}) 57 rpartFit <- train(medv ~ ., [all …]
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H A D | plot.varImp.train.Rd | 2 % Please edit documentation in R/plot.varImp.train.R 3 \name{plot.varImp.train} 4 \alias{plot.varImp.train} 5 \alias{ggplot.varImp.train} 8 \method{plot}{varImp.train}(x, top = dim(x$importance)[1], ...) 10 \method{ggplot}{varImp.train}( 28 \pkg{ggplot2} generic method} 35 "varImp.train". More info will be forthcoming.
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H A D | update.train.Rd | 3 \name{update.train} 4 \alias{update.train} 7 \method{update}{train}(object, param = NULL, ...) 10 \item{object}{an object of class \code{\link{train}}} 17 a new \code{\link{train}} object 26 underlying package structure was different. To make old \code{\link{train}} 45 knnFit1 <- train(TrainData, TrainClasses, 46 method = "knn", 49 trControl = trainControl(method = "cv")) 55 \code{\link{train}}, \code{\link{trainControl}}
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H A D | confusionMatrix.train.Rd | 3 \name{confusionMatrix.train} 4 \alias{confusionMatrix.train} 9 \method{confusionMatrix}{train}( 17 \item{data}{An object of class \code{\link{train}}, \code{\link{rfe}}, 29 a list of class \code{confusionMatrix.train}, 36 Using a \code{\link{train}}, \code{\link{rfe}}, \code{\link{sbf}} object, 42 used for diagnostic purposes. For \code{\link{train}}, the matrix is 61 knnFit <- train(TrainData, TrainClasses, 62 method = "knn", 65 trControl = trainControl(method = "cv")) [all …]
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/dports/science/afni/afni-AFNI_21.3.16/src/R_scripts/ |
H A D | 3dSignatures.R | 992 train <- NULL globalVar 1004 train <- NULL globalVar 1014 train <- NULL globalVar 1024 if (is.null(train)) { 1039 save(train, lop_train, 1059 if (is.null(train)) { 1089 train <- NULL globalVar 1096 train <- NULL globalVar 1105 train <- NULL globalVar 1110 if (is.null(train)) { [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/model_selection/ |
H A D | _validation.py | 273 train, 530 train, 808 method="predict", 964 clone(estimator), X, y, train, test, verbose, fit_params, method 994 def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params, method): 1045 func = getattr(estimator, method) 1060 method=method, 1099 if method == "decision_function": 1131 default_values[method], 1661 train, [all …]
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/dports/math/R-cran-ddalpha/ddalpha/man/ |
H A D | ddalpha.classify.Rd | 11 ddalpha.classify(ddalpha, objects, subset, outsider.method = NULL, use.convex = NULL) 13 \method{predict}{ddalpha}(object, objects, subset, outsider.method = NULL, use.convex = NULL, ...) 17 DD\eqn{\alpha}-classifier (obtained by \code{\link{ddalpha.train}}). 25 \item{outsider.method}{ 26 …code{\link{ddalpha.train}}. If the treatment was specified using the argument \code{outsider.metho… 29 …de{NULL} the value specified in DD\eqn{\alpha}-classifier (in \code{\link{ddalpha.train}}) is used. 41 …acter string "Ignored" for the outsiders if "Ignore" was specified as the outsider treating method. 57 \code{\link{ddalpha.train}} to train the DD-classifier. 74 data <- list(train = trainData, test = testData) 78 ddalpha <- ddalpha.train(data$train, [all …]
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/dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/KeywordMetadata/ |
H A D | method-function_train | 2 UQ method leveraging a functional tensor train surrogate model. 5 Tensor train decompositions are approximations that exploit low rank structure 11 This method is a self-contained method alternative to the 13 to current method specifications for polynomial chaos and stochastic collocation. 14 In particular, this \c function_train method specification directly couples with a 16 \c function train surrogate model specification is not required as these options 17 have been embedded within the method specification. 23 method, 38 See_Also:: model-surrogate-global-function_train, method-polynomial_chaos, method-stoch_collocation
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/docs/ |
H A D | learner.md | 5 To train a learner pass a training dataset as argument to the `train()` method: 7 public train(Dataset $training) : void 11 $estimator->train($dataset); 15 …Calling the `train()` method on an already trained learner will erase its previous training. If yo…
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/dports/math/p5-Statistics-LTU/Statistics-LTU-2.8/ |
H A D | LTU.pod | 11 $ltu->train([1,3,2], $LTU_PLUS); 12 $ltu->train([-1,3,0], $LTU_MINUS); 67 B<train> method. $LTU_THRESHOLD (set to zero) may be used to compare 68 values returned from the B<test> method. 107 Destroys the LTU (undefines its substructures). This method is kept 123 Static method. Creates and returns a new LTU from I<filename>. 170 B<train> method. The B<train> method "trains" the LTU that an instance 171 belongs in a particular class. For each B<train> method, I<instance> must 175 method. A typical B<train> call looks like: 185 B<train(instance, value)> [all …]
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