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comparison ensemble.xml @ 35:19d6c2745d34 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
author bgruening
date Tue, 13 Apr 2021 17:40:39 +0000
parents af0523c606a7
children 6546d7c9f08b
comparison
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34:841a6cc5fc58 35:19d6c2745d34
1 <tool id="sklearn_ensemble" name="Ensemble methods" version="@VERSION@"> 1 <tool id="sklearn_ensemble" name="Ensemble methods" version="@VERSION@" profile="20.05">
2 <description>for classification and regression</description> 2 <description>for classification and regression</description>
3 <macros> 3 <macros>
4 <import>main_macros.xml</import> 4 <import>main_macros.xml</import>
5 </macros> 5 </macros>
6 <expand macro="python_requirements"/> 6 <expand macro="python_requirements" />
7 <expand macro="macro_stdio"/> 7 <expand macro="macro_stdio" />
8 <version_command>echo "@VERSION@"</version_command> 8 <version_command>echo "@VERSION@"</version_command>
9 <command><![CDATA[ 9 <command><![CDATA[
10 python "$ensemble_script" '$inputs' 10 python "$ensemble_script" '$inputs'
11 ]]> 11 ]]>
12 </command> 12 </command>
13 <configfiles> 13 <configfiles>
14 <inputs name="inputs"/> 14 <inputs name="inputs" />
15 <configfile name="ensemble_script"> 15 <configfile name="ensemble_script">
16 <![CDATA[ 16 <![CDATA[
17 import json 17 import json
18 import numpy as np 18 import numpy as np
19 import pandas 19 import pandas
20 import pickle 20 import pickle
21 import sys 21 import sys
97 <option value="RandomForestRegressor">Random forest regressor</option> 97 <option value="RandomForestRegressor">Random forest regressor</option>
98 <option value="AdaBoostRegressor">Ada boost regressor</option> 98 <option value="AdaBoostRegressor">Ada boost regressor</option>
99 <option value="GradientBoostingRegressor">Gradient Boosting Regressor</option> 99 <option value="GradientBoostingRegressor">Gradient Boosting Regressor</option>
100 </param> 100 </param>
101 <when value="RandomForestClassifier"> 101 <when value="RandomForestClassifier">
102 <expand macro="sl_mixed_input"/> 102 <expand macro="sl_mixed_input" />
103 <section name="options" title="Advanced Options" expanded="False"> 103 <section name="options" title="Advanced Options" expanded="False">
104 <expand macro="n_estimators" default_value="100"/> 104 <expand macro="n_estimators" default_value="100" />
105 <expand macro="criterion"/> 105 <expand macro="criterion" />
106 <expand macro="max_features"/> 106 <expand macro="max_features" />
107 <expand macro="max_depth"/> 107 <expand macro="max_depth" />
108 <expand macro="min_samples_split"/> 108 <expand macro="min_samples_split" />
109 <expand macro="min_samples_leaf"/> 109 <expand macro="min_samples_leaf" />
110 <expand macro="min_weight_fraction_leaf"/> 110 <expand macro="min_weight_fraction_leaf" />
111 <expand macro="max_leaf_nodes"/> 111 <expand macro="max_leaf_nodes" />
112 <expand macro="bootstrap"/> 112 <expand macro="bootstrap" />
113 <expand macro="warm_start" checked="false"/> 113 <expand macro="warm_start" checked="false" />
114 <expand macro="random_state"/> 114 <expand macro="random_state" />
115 <expand macro="oob_score"/> 115 <expand macro="oob_score" />
116 <!--class_weight=None--> 116 <!--class_weight=None-->
117 </section> 117 </section>
118 </when> 118 </when>
119 <when value="AdaBoostClassifier"> 119 <when value="AdaBoostClassifier">
120 <expand macro="sl_mixed_input"/> 120 <expand macro="sl_mixed_input" />
121 <section name="options" title="Advanced Options" expanded="False"> 121 <section name="options" title="Advanced Options" expanded="False">
122 <!--base_estimator=None--> 122 <!--base_estimator=None-->
123 <expand macro="n_estimators" default_value="50"/> 123 <expand macro="n_estimators" default_value="50" />
124 <expand macro="learning_rate"/> 124 <expand macro="learning_rate" />
125 <param argument="algorithm" type="select" label="Boosting algorithm" help=" "> 125 <param argument="algorithm" type="select" label="Boosting algorithm" help=" ">
126 <option value="SAMME.R" selected="true">SAMME.R</option> 126 <option value="SAMME.R" selected="true">SAMME.R</option>
127 <option value="SAMME">SAMME</option> 127 <option value="SAMME">SAMME</option>
128 </param> 128 </param>
129 <expand macro="random_state"/> 129 <expand macro="random_state" />
130 </section> 130 </section>
131 </when> 131 </when>
132 <when value="GradientBoostingClassifier"> 132 <when value="GradientBoostingClassifier">
133 <expand macro="sl_mixed_input"/> 133 <expand macro="sl_mixed_input" />
134 <section name="options" title="Advanced Options" expanded="False"> 134 <section name="options" title="Advanced Options" expanded="False">
135 <!--base_estimator=None--> 135 <!--base_estimator=None-->
136 <param argument="loss" type="select" label="Loss function"> 136 <param argument="loss" type="select" label="Loss function">
137 <option value="deviance" selected="true">deviance - logistic regression with probabilistic outputs</option> 137 <option value="deviance" selected="true">deviance - logistic regression with probabilistic outputs</option>
138 <option value="exponential">exponential - gradient boosting recovers the AdaBoost algorithm</option> 138 <option value="exponential">exponential - gradient boosting recovers the AdaBoost algorithm</option>
139 </param> 139 </param>
140 <expand macro="learning_rate" default_value='0.1'/> 140 <expand macro="learning_rate" default_value='0.1' />
141 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/> 141 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform" />
142 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/> 142 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators" />
143 <expand macro="criterion2"> 143 <expand macro="criterion2">
144 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option> 144 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
145 </expand> 145 </expand>
146 <expand macro="min_samples_split" type="float"/> 146 <expand macro="min_samples_split" type="float" />
147 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/> 147 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node" />
148 <expand macro="min_weight_fraction_leaf"/> 148 <expand macro="min_weight_fraction_leaf" />
149 <expand macro="subsample"/> 149 <expand macro="subsample" />
150 <expand macro="max_features"/> 150 <expand macro="max_features" />
151 <expand macro="max_leaf_nodes"/> 151 <expand macro="max_leaf_nodes" />
152 <expand macro="min_impurity_decrease"/> 152 <expand macro="min_impurity_decrease" />
153 <expand macro="verbose"/> 153 <expand macro="verbose" />
154 <expand macro="warm_start" checked="false"/> 154 <expand macro="warm_start" checked="false" />
155 <expand macro="random_state"/> 155 <expand macro="random_state" />
156 <expand macro="presort"/> 156 <expand macro="presort" />
157 </section> 157 </section>
158 </when> 158 </when>
159 <when value="RandomForestRegressor"> 159 <when value="RandomForestRegressor">
160 <expand macro="sl_mixed_input"/> 160 <expand macro="sl_mixed_input" />
161 <section name="options" title="Advanced Options" expanded="False"> 161 <section name="options" title="Advanced Options" expanded="False">
162 <expand macro="n_estimators" default_value="100"/> 162 <expand macro="n_estimators" default_value="100" />
163 <expand macro="criterion2"/> 163 <expand macro="criterion2" />
164 <expand macro="max_features"/> 164 <expand macro="max_features" />
165 <expand macro="max_depth"/> 165 <expand macro="max_depth" />
166 <expand macro="min_samples_split"/> 166 <expand macro="min_samples_split" />
167 <expand macro="min_samples_leaf"/> 167 <expand macro="min_samples_leaf" />
168 <expand macro="min_weight_fraction_leaf"/> 168 <expand macro="min_weight_fraction_leaf" />
169 <expand macro="max_leaf_nodes"/> 169 <expand macro="max_leaf_nodes" />
170 <expand macro="min_impurity_decrease"/> 170 <expand macro="min_impurity_decrease" />
171 <expand macro="bootstrap"/> 171 <expand macro="bootstrap" />
172 <expand macro="oob_score"/> 172 <expand macro="oob_score" />
173 <expand macro="random_state"/> 173 <expand macro="random_state" />
174 <expand macro="verbose"/> 174 <expand macro="verbose" />
175 <expand macro="warm_start" checked="false"/> 175 <expand macro="warm_start" checked="false" />
176 </section> 176 </section>
177 </when> 177 </when>
178 <when value="AdaBoostRegressor"> 178 <when value="AdaBoostRegressor">
179 <expand macro="sl_mixed_input"/> 179 <expand macro="sl_mixed_input" />
180 <section name="options" title="Advanced Options" expanded="False"> 180 <section name="options" title="Advanced Options" expanded="False">
181 <!--base_estimator=None--> 181 <!--base_estimator=None-->
182 <expand macro="n_estimators" default_value="50"/> 182 <expand macro="n_estimators" default_value="50" />
183 <expand macro="learning_rate"/> 183 <expand macro="learning_rate" />
184 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. "> 184 <param argument="loss" type="select" label="Loss function" optional="true" help="Used when updating the weights after each boosting iteration. ">
185 <option value="linear" selected="true">linear</option> 185 <option value="linear" selected="true">linear</option>
186 <option value="square">square</option> 186 <option value="square">square</option>
187 <option value="exponential">exponential</option> 187 <option value="exponential">exponential</option>
188 </param> 188 </param>
189 <expand macro="random_state"/> 189 <expand macro="random_state" />
190 </section> 190 </section>
191 </when> 191 </when>
192 <when value="GradientBoostingRegressor"> 192 <when value="GradientBoostingRegressor">
193 <expand macro="sl_mixed_input"/> 193 <expand macro="sl_mixed_input" />
194 <section name="options" title="Advanced Options" expanded="False"> 194 <section name="options" title="Advanced Options" expanded="False">
195 <param argument="loss" type="select" label="Loss function"> 195 <param argument="loss" type="select" label="Loss function">
196 <option value="ls" selected="true">ls - least squares regression</option> 196 <option value="ls" selected="true">ls - least squares regression</option>
197 <option value="lad">lad - least absolute deviation</option> 197 <option value="lad">lad - least absolute deviation</option>
198 <option value="huber">huber - combination of least squares regression and least absolute deviation</option> 198 <option value="huber">huber - combination of least squares regression and least absolute deviation</option>
199 <option value="quantile">quantile - use alpha to specify the quantile</option> 199 <option value="quantile">quantile - use alpha to specify the quantile</option>
200 </param> 200 </param>
201 <expand macro="learning_rate" default_value="0.1"/> 201 <expand macro="learning_rate" default_value="0.1" />
202 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/> 202 <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform" />
203 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/> 203 <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators" />
204 <expand macro="criterion2"> 204 <expand macro="criterion2">
205 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option> 205 <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
206 </expand> 206 </expand>
207 <expand macro="min_samples_split" type="float"/> 207 <expand macro="min_samples_split" type="float" />
208 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/> 208 <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node" />
209 <expand macro="min_weight_fraction_leaf"/> 209 <expand macro="min_weight_fraction_leaf" />
210 <expand macro="subsample"/> 210 <expand macro="subsample" />
211 <expand macro="max_features"/> 211 <expand macro="max_features" />
212 <expand macro="max_leaf_nodes"/> 212 <expand macro="max_leaf_nodes" />
213 <expand macro="min_impurity_decrease"/> 213 <expand macro="min_impurity_decrease" />
214 <param argument="alpha" type="float" value="0.9" label="alpha" help="The alpha-quantile of the huber loss function and the quantile loss function" /> 214 <param argument="alpha" type="float" value="0.9" label="alpha" help="The alpha-quantile of the huber loss function and the quantile loss function" />
215 <!--base_estimator=None--> 215 <!--base_estimator=None-->
216 <expand macro="verbose"/> 216 <expand macro="verbose" />
217 <expand macro="warm_start" checked="false"/> 217 <expand macro="warm_start" checked="false" />
218 <expand macro="random_state"/> 218 <expand macro="random_state" />
219 <expand macro="presort"/> 219 <expand macro="presort" />
220 </section> 220 </section>
221 </when> 221 </when>
222 </expand> 222 </expand>
223 </inputs> 223 </inputs>
224 224
225 <expand macro="output"/> 225 <expand macro="output" />
226 226
227 <tests> 227 <tests>
228 <test> 228 <test>
229 <param name="infile1" value="train.tabular" ftype="tabular"/> 229 <param name="infile1" value="train.tabular" ftype="tabular" />
230 <param name="infile2" value="train.tabular" ftype="tabular"/> 230 <param name="infile2" value="train.tabular" ftype="tabular" />
231 <param name="col1" value="1,2,3,4"/> 231 <param name="col1" value="1,2,3,4" />
232 <param name="col2" value="5"/> 232 <param name="col2" value="5" />
233 <param name="selected_task" value="train"/> 233 <param name="selected_task" value="train" />
234 <param name="selected_algorithm" value="RandomForestClassifier"/> 234 <param name="selected_algorithm" value="RandomForestClassifier" />
235 <param name="random_state" value="10"/> 235 <param name="random_state" value="10" />
236 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="5"/> 236 <output name="outfile_fit" file="rfc_model01" compare="sim_size" delta="5" />
237 </test> 237 </test>
238 <test> 238 <test>
239 <param name="infile_model" value="rfc_model01" ftype="zip"/> 239 <param name="infile_model" value="rfc_model01" ftype="zip" />
240 <param name="infile_data" value="test.tabular" ftype="tabular"/> 240 <param name="infile_data" value="test.tabular" ftype="tabular" />
241 <param name="selected_task" value="load"/> 241 <param name="selected_task" value="load" />
242 <output name="outfile_predict" file="rfc_result01"/> 242 <output name="outfile_predict" file="rfc_result01" />
243 </test> 243 </test>
244 <test> 244 <test>
245 <param name="infile1" value="regression_train.tabular" ftype="tabular"/> 245 <param name="infile1" value="regression_train.tabular" ftype="tabular" />
246 <param name="infile2" value="regression_train.tabular" ftype="tabular"/> 246 <param name="infile2" value="regression_train.tabular" ftype="tabular" />
247 <param name="col1" value="1,2,3,4,5"/> 247 <param name="col1" value="1,2,3,4,5" />
248 <param name="col2" value="6"/> 248 <param name="col2" value="6" />
249 <param name="selected_task" value="train"/> 249 <param name="selected_task" value="train" />
250 <param name="selected_algorithm" value="RandomForestRegressor"/> 250 <param name="selected_algorithm" value="RandomForestRegressor" />
251 <param name="random_state" value="10"/> 251 <param name="random_state" value="10" />
252 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="5"/> 252 <output name="outfile_fit" file="rfr_model01" compare="sim_size" delta="5" />
253 </test> 253 </test>
254 <test> 254 <test>
255 <param name="infile_model" value="rfr_model01" ftype="zip"/> 255 <param name="infile_model" value="rfr_model01" ftype="zip" />
256 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/> 256 <param name="infile_data" value="regression_test.tabular" ftype="tabular" />
257 <param name="selected_task" value="load"/> 257 <param name="selected_task" value="load" />
258 <output name="outfile_predict" file="rfr_result01"/> 258 <output name="outfile_predict" file="rfr_result01" />
259 </test> 259 </test>
260 <test> 260 <test>
261 <param name="infile1" value="regression_X.tabular" ftype="tabular"/> 261 <param name="infile1" value="regression_X.tabular" ftype="tabular" />
262 <param name="infile2" value="regression_y.tabular" ftype="tabular"/> 262 <param name="infile2" value="regression_y.tabular" ftype="tabular" />
263 <param name="header1" value="True"/> 263 <param name="header1" value="True" />
264 <param name="selected_column_selector_option" value="all_columns"/> 264 <param name="selected_column_selector_option" value="all_columns" />
265 <param name="header2" value="True"/> 265 <param name="header2" value="True" />
266 <param name="col2" value="1"/> 266 <param name="col2" value="1" />
267 <param name="selected_task" value="train"/> 267 <param name="selected_task" value="train" />
268 <param name="selected_algorithm" value="GradientBoostingRegressor"/> 268 <param name="selected_algorithm" value="GradientBoostingRegressor" />
269 <param name="max_features" value="number_input"/> 269 <param name="max_features" value="number_input" />
270 <param name="num_max_features" value="0.5"/> 270 <param name="num_max_features" value="0.5" />
271 <param name="random_state" value="42"/> 271 <param name="random_state" value="42" />
272 <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="5"/> 272 <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="5" />
273 </test> 273 </test>
274 <test> 274 <test>
275 <param name="infile_model" value="gbr_model01" ftype="zip"/> 275 <param name="infile_model" value="gbr_model01" ftype="zip" />
276 <param name="infile_data" value="regression_test_X.tabular" ftype="tabular"/> 276 <param name="infile_data" value="regression_test_X.tabular" ftype="tabular" />
277 <param name="selected_task" value="load"/> 277 <param name="selected_task" value="load" />
278 <param name="header" value="True"/> 278 <param name="header" value="True" />
279 <output name="outfile_predict" file="gbr_prediction_result01.tabular"/> 279 <output name="outfile_predict" file="gbr_prediction_result01.tabular" />
280 </test> 280 </test>
281 <test> 281 <test>
282 <param name="infile1" value="train.tabular" ftype="tabular"/> 282 <param name="infile1" value="train.tabular" ftype="tabular" />
283 <param name="infile2" value="train.tabular" ftype="tabular"/> 283 <param name="infile2" value="train.tabular" ftype="tabular" />
284 <param name="col1" value="1,2,3,4"/> 284 <param name="col1" value="1,2,3,4" />
285 <param name="col2" value="5"/> 285 <param name="col2" value="5" />
286 <param name="selected_task" value="train"/> 286 <param name="selected_task" value="train" />
287 <param name="selected_algorithm" value="GradientBoostingClassifier"/> 287 <param name="selected_algorithm" value="GradientBoostingClassifier" />
288 <output name="outfile_fit" file="gbc_model01" compare="sim_size" delta="5"/> 288 <output name="outfile_fit" file="gbc_model01" compare="sim_size" delta="5" />
289 </test> 289 </test>
290 <test> 290 <test>
291 <param name="infile_model" value="gbc_model01" ftype="zip"/> 291 <param name="infile_model" value="gbc_model01" ftype="zip" />
292 <param name="infile_data" value="test.tabular" ftype="tabular"/> 292 <param name="infile_data" value="test.tabular" ftype="tabular" />
293 <param name="selected_task" value="load"/> 293 <param name="selected_task" value="load" />
294 <output name="outfile_predict" file="gbc_result01"/> 294 <output name="outfile_predict" file="gbc_result01" />
295 </test> 295 </test>
296 <test> 296 <test>
297 <param name="infile1" value="train.tabular" ftype="tabular"/> 297 <param name="infile1" value="train.tabular" ftype="tabular" />
298 <param name="infile2" value="train.tabular" ftype="tabular"/> 298 <param name="infile2" value="train.tabular" ftype="tabular" />
299 <param name="col1" value="1,2,3,4"/> 299 <param name="col1" value="1,2,3,4" />
300 <param name="col2" value="5"/> 300 <param name="col2" value="5" />
301 <param name="selected_task" value="train"/> 301 <param name="selected_task" value="train" />
302 <param name="selected_algorithm" value="AdaBoostClassifier"/> 302 <param name="selected_algorithm" value="AdaBoostClassifier" />
303 <param name="random_state" value="10"/> 303 <param name="random_state" value="10" />
304 <output name="outfile_fit" file="abc_model01" compare="sim_size" delta="5"/> 304 <output name="outfile_fit" file="abc_model01" compare="sim_size" delta="5" />
305 </test> 305 </test>
306 <test> 306 <test>
307 <param name="infile_model" value="abc_model01" ftype="zip"/> 307 <param name="infile_model" value="abc_model01" ftype="zip" />
308 <param name="infile_data" value="test.tabular" ftype="tabular"/> 308 <param name="infile_data" value="test.tabular" ftype="tabular" />
309 <param name="selected_task" value="load"/> 309 <param name="selected_task" value="load" />
310 <output name="outfile_predict" file="abc_result01"/> 310 <output name="outfile_predict" file="abc_result01" />
311 </test> 311 </test>
312 <test> 312 <test>
313 <param name="infile1" value="regression_train.tabular" ftype="tabular"/> 313 <param name="infile1" value="regression_train.tabular" ftype="tabular" />
314 <param name="infile2" value="regression_train.tabular" ftype="tabular"/> 314 <param name="infile2" value="regression_train.tabular" ftype="tabular" />
315 <param name="col1" value="1,2,3,4,5"/> 315 <param name="col1" value="1,2,3,4,5" />
316 <param name="col2" value="6"/> 316 <param name="col2" value="6" />
317 <param name="selected_task" value="train"/> 317 <param name="selected_task" value="train" />
318 <param name="selected_algorithm" value="AdaBoostRegressor"/> 318 <param name="selected_algorithm" value="AdaBoostRegressor" />
319 <param name="random_state" value="10"/> 319 <param name="random_state" value="10" />
320 <output name="outfile_fit" file="abr_model01" compare="sim_size" delta="5"/> 320 <output name="outfile_fit" file="abr_model01" compare="sim_size" delta="5" />
321 </test> 321 </test>
322 <test> 322 <test>
323 <param name="infile_model" value="abr_model01" ftype="zip"/> 323 <param name="infile_model" value="abr_model01" ftype="zip" />
324 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/> 324 <param name="infile_data" value="regression_test.tabular" ftype="tabular" />
325 <param name="selected_task" value="load"/> 325 <param name="selected_task" value="load" />
326 <output name="outfile_predict" file="abr_result01"/> 326 <output name="outfile_predict" file="abr_result01" />
327 </test> 327 </test>
328 </tests> 328 </tests>
329 <help><![CDATA[ 329 <help><![CDATA[
330 ***What it does*** 330 ***What it does***
331 The goal of ensemble methods is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. This tool offers two sets of ensemble algorithms for classification and regression: random forests and ADA boosting which are based on sklearn.ensemble library from Scikit-learn. Here you can find out about the input, output and methods presented in the tools. For information about ensemble methods and parameters settings please refer to `Scikit-learn ensemble`_. 331 The goal of ensemble methods is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. This tool offers two sets of ensemble algorithms for classification and regression: random forests and ADA boosting which are based on sklearn.ensemble library from Scikit-learn. Here you can find out about the input, output and methods presented in the tools. For information about ensemble methods and parameters settings please refer to `Scikit-learn ensemble`_.
388 388
389 389
390 **3 - Prediction output** 390 **3 - Prediction output**
391 The tool predicts the class labels for new samples and adds them as the last column to the prediction dataset. The new dataset then is output as a tabular file. The prediction output format should look like the training dataset. 391 The tool predicts the class labels for new samples and adds them as the last column to the prediction dataset. The new dataset then is output as a tabular file. The prediction output format should look like the training dataset.
392 392
393 ]]></help> 393 ]]> </help>
394 <expand macro="sklearn_citation"/> 394 <expand macro="sklearn_citation" />
395 </tool> 395 </tool>