Python Machine Learning

Grid Search

Hyperparameter tuning

Grid Search

The majority of machine learning models contain parameters that can be adjusted to vary how the model learns. These parameters are called hyperparameters.

Grid Search Example

from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV

iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}

svc = svm.SVC()
clf = GridSearchCV(svc, parameters)
clf.fit(iris.data, iris.target)

print(clf.best_params_)