Mercurial > repos > bgruening > sklearn_ensemble

comparison ensemble.xml @ 26:8d0d31567045 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
author bgruening
date Thu, 11 Oct 2018 03:22:26 -0400
parents 18e29854c803
children 5af731ab6995
comparison
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25:18e29854c803 26:8d0d31567045
290 <param name="infile_model" value="gbc_model01" ftype="zip"/> 290 <param name="infile_model" value="gbc_model01" ftype="zip"/>
291 <param name="infile_data" value="test.tabular" ftype="tabular"/> 291 <param name="infile_data" value="test.tabular" ftype="tabular"/>
292 <param name="selected_task" value="load"/> 292 <param name="selected_task" value="load"/>
293 <output name="outfile_predict" file="gbc_result01" compare="sim_size" delta="500"/> 293 <output name="outfile_predict" file="gbc_result01" compare="sim_size" delta="500"/>
294 </test> 294 </test>
295 <test>
296 <param name="infile1" value="train.tabular" ftype="tabular"/>
297 <param name="infile2" value="train.tabular" ftype="tabular"/>
298 <param name="col1" value="1,2,3,4"/>
299 <param name="col2" value="5"/>
300 <param name="selected_task" value="train"/>
301 <param name="selected_algorithm" value="AdaBoostClassifier"/>
302 <param name="random_state" value="10"/>
303 <output name="outfile_fit" file="abc_model01" compare="sim_size" delta="500"/>
304 </test>
305 <test>
306 <param name="infile_model" value="abc_model01" ftype="zip"/>
307 <param name="infile_data" value="test.tabular" ftype="tabular"/>
308 <param name="selected_task" value="load"/>
309 <output name="outfile_predict" file="abc_result01" compare="sim_size" delta="500"/>
310 </test>
311 <test>
312 <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
313 <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
314 <param name="col1" value="1,2,3,4,5"/>
315 <param name="col2" value="6"/>
316 <param name="selected_task" value="train"/>
317 <param name="selected_algorithm" value="AdaBoostRegressor"/>
318 <param name="random_state" value="10"/>
319 <output name="outfile_fit" file="abr_model01" compare="sim_size" delta="500"/>
320 </test>
321 <test>
322 <param name="infile_model" value="abr_model01" ftype="zip"/>
323 <param name="infile_data" value="regression_test.tabular" ftype="tabular"/>
324 <param name="selected_task" value="load"/>
325 <output name="outfile_predict" file="abr_result01" compare="sim_size" delta="500"/>
326 </test>
295 </tests> 327 </tests>
296 <help><![CDATA[ 328 <help><![CDATA[
297 ***What it does*** 329 ***What it does***
298 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`_. 330 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`_.
299 331