Classifier Helper Module¶
Cross-validation helper module.
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class
mriqc.classifier.helper.
CVHelper
(X=None, Y=None, load_clf=None, param_file=None, n_jobs=- 1, site_label='site', rate_label=None, scorer='roc_auc', b_leaveout=False, multiclass=False, verbosity=0, split='kfold', debug=False, model='rfc', basename=None, nested_cv=False, nested_cv_kfold=False, permutation_test=0)[source]¶ Bases:
mriqc.classifier.helper.CVHelperBase
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property
Xtest
¶
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property
estimator
¶
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evaluate
(scoring=None, matrix=False, save_roc=False, save_pred=False)[source]¶ Evaluate the internal estimator on the test data
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load
(filehandler)[source]¶ UnPickle the estimator, adding the feature names http://scikit-learn.org/stable/modules/model_persistence.html
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predict
(X, thres=0.5, return_proba=True)[source]¶ Predict class for X. The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.
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save
(suffix='estimator', compress=3)[source]¶ Pickle the estimator, adding the feature names http://scikit-learn.org/stable/modules/model_persistence.html
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property
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class
mriqc.classifier.helper.
CVHelperBase
(X, Y, param_file=None, n_jobs=- 1, site_label='site', rate_label=None, rate_selection='random', scorer='roc_auc', multiclass=False, verbosity=0, debug=False)[source]¶ Bases:
object
A base helper to build cross-validation schemes
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property
ftnames
¶
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property
rate_column
¶
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property