-
-
Notifications
You must be signed in to change notification settings - Fork 15
/
mlr_learners_regr.ranger.Rd
250 lines (227 loc) · 10.6 KB
/
mlr_learners_regr.ranger.Rd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LearnerRegrRanger.R
\name{mlr_learners_regr.ranger}
\alias{mlr_learners_regr.ranger}
\alias{LearnerRegrRanger}
\title{Ranger Regression Learner}
\description{
Random regression forest.
Calls \code{\link[ranger:ranger]{ranger::ranger()}} from package \CRANpkg{ranger}.
}
\section{Dictionary}{
This \link{Learner} can be instantiated via the \link[mlr3misc:Dictionary]{dictionary} \link{mlr_learners} or with the associated sugar function \code{\link[=lrn]{lrn()}}:
\if{html}{\out{<div class="sourceCode">}}\preformatted{mlr_learners$get("regr.ranger")
lrn("regr.ranger")
}\if{html}{\out{</div>}}
}
\section{Meta Information}{
\itemize{
\item Task type: \dQuote{regr}
\item Predict Types: \dQuote{response}, \dQuote{se}
\item Feature Types: \dQuote{logical}, \dQuote{integer}, \dQuote{numeric}, \dQuote{character}, \dQuote{factor}, \dQuote{ordered}
\item Required Packages: \CRANpkg{mlr3}, \CRANpkg{mlr3learners}, \CRANpkg{ranger}
}
}
\section{Parameters}{
\tabular{lllll}{
Id \tab Type \tab Default \tab Levels \tab Range \cr
alpha \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
always.split.variables \tab untyped \tab - \tab \tab - \cr
holdout \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab - \cr
keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
max.depth \tab integer \tab NULL \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr
min.bucket \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr
min.node.size \tab integer \tab 5 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr
minprop \tab numeric \tab 0.1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
mtry \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr
mtry.ratio \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr
node.stats \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
num.random.splits \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr
num.threads \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr
num.trees \tab integer \tab 500 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr
oob.error \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr
quantreg \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
regularization.factor \tab untyped \tab 1 \tab \tab - \cr
regularization.usedepth \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
replace \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr
respect.unordered.factors \tab character \tab ignore \tab ignore, order, partition \tab - \cr
sample.fraction \tab numeric \tab - \tab \tab \eqn{[0, 1]}{[0, 1]} \cr
save.memory \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
scale.permutation.importance \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr
se.method \tab character \tab infjack \tab jack, infjack \tab - \cr
seed \tab integer \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr
split.select.weights \tab untyped \tab NULL \tab \tab - \cr
splitrule \tab character \tab variance \tab variance, extratrees, maxstat \tab - \cr
verbose \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr
write.forest \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr
}
}
\section{Custom mlr3 parameters}{
\itemize{
\item \code{mtry}:
\itemize{
\item This hyperparameter can alternatively be set via our hyperparameter \code{mtry.ratio}
as \code{mtry = max(ceiling(mtry.ratio * n_features), 1)}.
Note that \code{mtry} and \code{mtry.ratio} are mutually exclusive.
}
}
}
\section{Initial parameter values}{
\itemize{
\item \code{num.threads}:
\itemize{
\item Actual default: \code{NULL}, triggering auto-detection of the number of CPUs.
\item Adjusted value: 1.
\item Reason for change: Conflicting with parallelization via \CRANpkg{future}.
}
}
}
\examples{
if (requireNamespace("ranger", quietly = TRUE)) {
# Define the Learner and set parameter values
learner = lrn("regr.ranger")
print(learner)
# Define a Task
task = tsk("mtcars")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# print the model
print(learner$model)
# importance method
if("importance" \%in\% learner$properties) print(learner$importance)
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
}
}
\references{
Wright, N. M, Ziegler, Andreas (2017).
\dQuote{ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.}
\emph{Journal of Statistical Software}, \bold{77}(1), 1--17.
\doi{10.18637/jss.v077.i01}.
Breiman, Leo (2001).
\dQuote{Random Forests.}
\emph{Machine Learning}, \bold{45}(1), 5--32.
ISSN 1573-0565, \doi{10.1023/A:1010933404324}.
}
\seealso{
\itemize{
\item Chapter in the \href{https://mlr3book.mlr-org.com/}{mlr3book}:
\url{https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners}
\item Package \href{https://github.com/mlr-org/mlr3extralearners}{mlr3extralearners} for more learners.
\item \link[mlr3misc:Dictionary]{Dictionary} of \link[=Learner]{Learners}: \link{mlr_learners}
\item \code{as.data.table(mlr_learners)} for a table of available \link[=Learner]{Learners} in the running session (depending on the loaded packages).
\item \CRANpkg{mlr3pipelines} to combine learners with pre- and postprocessing steps.
\item Extension packages for additional task types:
\itemize{
\item \CRANpkg{mlr3proba} for probabilistic supervised regression and survival analysis.
\item \CRANpkg{mlr3cluster} for unsupervised clustering.
}
\item \CRANpkg{mlr3tuning} for tuning of hyperparameters, \CRANpkg{mlr3tuningspaces}
for established default tuning spaces.
}
Other Learner:
\code{\link{mlr_learners_classif.cv_glmnet}},
\code{\link{mlr_learners_classif.glmnet}},
\code{\link{mlr_learners_classif.kknn}},
\code{\link{mlr_learners_classif.lda}},
\code{\link{mlr_learners_classif.log_reg}},
\code{\link{mlr_learners_classif.multinom}},
\code{\link{mlr_learners_classif.naive_bayes}},
\code{\link{mlr_learners_classif.nnet}},
\code{\link{mlr_learners_classif.qda}},
\code{\link{mlr_learners_classif.ranger}},
\code{\link{mlr_learners_classif.svm}},
\code{\link{mlr_learners_classif.xgboost}},
\code{\link{mlr_learners_regr.cv_glmnet}},
\code{\link{mlr_learners_regr.glmnet}},
\code{\link{mlr_learners_regr.kknn}},
\code{\link{mlr_learners_regr.km}},
\code{\link{mlr_learners_regr.lm}},
\code{\link{mlr_learners_regr.nnet}},
\code{\link{mlr_learners_regr.svm}},
\code{\link{mlr_learners_regr.xgboost}}
}
\concept{Learner}
\section{Super classes}{
\code{\link[mlr3:Learner]{mlr3::Learner}} -> \code{\link[mlr3:LearnerRegr]{mlr3::LearnerRegr}} -> \code{LearnerRegrRanger}
}
\section{Methods}{
\subsection{Public methods}{
\itemize{
\item \href{#method-LearnerRegrRanger-new}{\code{LearnerRegrRanger$new()}}
\item \href{#method-LearnerRegrRanger-importance}{\code{LearnerRegrRanger$importance()}}
\item \href{#method-LearnerRegrRanger-oob_error}{\code{LearnerRegrRanger$oob_error()}}
\item \href{#method-LearnerRegrRanger-clone}{\code{LearnerRegrRanger$clone()}}
}
}
\if{html}{\out{
<details><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="base_learner"><a href='../../mlr3/html/Learner.html#method-Learner-base_learner'><code>mlr3::Learner$base_learner()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="format"><a href='../../mlr3/html/Learner.html#method-Learner-format'><code>mlr3::Learner$format()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="help"><a href='../../mlr3/html/Learner.html#method-Learner-help'><code>mlr3::Learner$help()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="predict"><a href='../../mlr3/html/Learner.html#method-Learner-predict'><code>mlr3::Learner$predict()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="predict_newdata"><a href='../../mlr3/html/Learner.html#method-Learner-predict_newdata'><code>mlr3::Learner$predict_newdata()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="print"><a href='../../mlr3/html/Learner.html#method-Learner-print'><code>mlr3::Learner$print()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="reset"><a href='../../mlr3/html/Learner.html#method-Learner-reset'><code>mlr3::Learner$reset()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="train"><a href='../../mlr3/html/Learner.html#method-Learner-train'><code>mlr3::Learner$train()</code></a></span></li>
</ul>
</details>
}}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-LearnerRegrRanger-new"></a>}}
\if{latex}{\out{\hypertarget{method-LearnerRegrRanger-new}{}}}
\subsection{Method \code{new()}}{
Creates a new instance of this \link[R6:R6Class]{R6} class.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LearnerRegrRanger$new()}\if{html}{\out{</div>}}
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-LearnerRegrRanger-importance"></a>}}
\if{latex}{\out{\hypertarget{method-LearnerRegrRanger-importance}{}}}
\subsection{Method \code{importance()}}{
The importance scores are extracted from the model slot \code{variable.importance}.
Parameter \code{importance.mode} must be set to \code{"impurity"}, \code{"impurity_corrected"}, or
\code{"permutation"}
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LearnerRegrRanger$importance()}\if{html}{\out{</div>}}
}
\subsection{Returns}{
Named \code{numeric()}.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-LearnerRegrRanger-oob_error"></a>}}
\if{latex}{\out{\hypertarget{method-LearnerRegrRanger-oob_error}{}}}
\subsection{Method \code{oob_error()}}{
The out-of-bag error, extracted from model slot \code{prediction.error}.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LearnerRegrRanger$oob_error()}\if{html}{\out{</div>}}
}
\subsection{Returns}{
\code{numeric(1)}.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-LearnerRegrRanger-clone"></a>}}
\if{latex}{\out{\hypertarget{method-LearnerRegrRanger-clone}{}}}
\subsection{Method \code{clone()}}{
The objects of this class are cloneable with this method.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LearnerRegrRanger$clone(deep = FALSE)}\if{html}{\out{</div>}}
}
\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{deep}}{Whether to make a deep clone.}
}
\if{html}{\out{</div>}}
}
}
}