python-scikit-learnHow to disable cross validation in grid search CV
from sklearn import svm, datasets, model_selection
iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
clf = model_selection.GridSearchCV(svm.SVC(), parameters, cv=[(slice(None), slice(None))])
clf.fit(iris.data, iris.target)ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
parametersparameters dictionary to run grid search accross | .GridSearchCV(creates GridSearchCV model |
svm.SVC()use SVC model as an estimator | .fit(train transformation model |
[(slice(None), slice(None))]will disable cross validation | |
Usage example
from sklearn import svm, datasets, model_selection
iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
clf = model_selection.GridSearchCV(svm.SVC(), parameters, cv=[(slice(None), slice(None))])
clf.fit(iris.data, iris.target)
print(clf.best_estimator_)output
SVC(C=1, kernel='linear')
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