Mercurial > repos > bgruening > sklearn_ensemble
comparison ensemble.xml @ 22:2e69c6ca6e91 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c64ccc5850c8e061a95fb64e07ed388384e82393
author | bgruening |
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date | Thu, 11 Oct 2018 03:34:03 -0400 |
parents | 9ce3e347506c |
children | 39ae276e75d9 |
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21:9ce3e347506c | 22:2e69c6ca6e91 |
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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 |