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