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/dports/devel/R-cran-caret/caret/tests/testthat/
H A Dtrim_rpart.R10 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,
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H A Dtest_ptypes.R11 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)
H A Dtrim_train.R10 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,
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H A Dtrim_C5.R13 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,
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H A Dtrim_glm.R10 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,
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H A Dtrim_bayesglm.R10 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,
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H A Dtrim_treebag.R10 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,
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H A Dtrim_glmnet.R10 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,
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H A Dtest_minimal.R12 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")))
H A Dtest_bad_class_options.R9 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",
H A Dtest_tibble.R14 ctrl <- trainControl(method = "repeatedcv",
21 train(
24 method = "glm",
36 train(
39 method = "glm",
50 train(
53 method = "glm",
/dports/devel/R-cran-caret/caret/man/
H A Dtrain.Rd3 \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",
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H A Dprint.train.Rd2 % 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}}
H A Dpredict.train.Rd3 % 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",
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H A Dplot.train.Rd2 % 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",
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H A Dhistogram.train.Rd2 % 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 ~ .,
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H A Dplot.varImp.train.Rd2 % 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.
H A Dupdate.train.Rd3 \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}}
H A DconfusionMatrix.train.Rd3 \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"))
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/dports/science/afni/afni-AFNI_21.3.16/src/R_scripts/
H A D3dSignatures.R992 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)) {
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/model_selection/
H A D_validation.py273 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,
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/dports/math/R-cran-ddalpha/ddalpha/man/
H A Dddalpha.classify.Rd11 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,
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/dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/KeywordMetadata/
H A Dmethod-function_train2 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
/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/docs/
H A Dlearner.md5 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…
/dports/math/p5-Statistics-LTU/Statistics-LTU-2.8/
H A DLTU.pod11 $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)>
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