def computeCrossValidation(self): from sklearn.model_selection import cross_validate X, y = ClassificationModel.preprocessDataCrossValidation(self.args, True) classifier = LogisticRegression.computeModel(X, y, self.args.solver) cv_results = cross_validate(classifier, X, y, cv=self.args.k_fold_cross_validation) if(self.args.print_accuracy): print(cv_results) return cv_results
def computeCrossValidation(self): from sklearn.model_selection import cross_validate X, y = ClassificationModel.preprocessDataCrossValidation(self.args, False) classifier = RandomForest.computeModel(X, y, self.args.n_estimators, self.args.criterion) cv_results = cross_validate(classifier, X, y, cv=self.args.k_fold_cross_validation) if(self.args.print_accuracy): print(cv_results) return cv_results
def computeCrossValidation(self): from sklearn.model_selection import cross_validate X, y = ClassificationModel.preprocessDataCrossValidation(self.args, True) classifier = KNN.computeModel(X, y, self.args.n_neighbors, self.args.power_parameter_minkowski_metric) cv_results = cross_validate(classifier, X, y, cv=self.args.k_fold_cross_validation) if(self.args.print_accuracy): print(cv_results) return cv_results