CODEWITHSUNDEEP
scikit-learn
Borys
Borys

Reputation: 1423

Grid-search-cross-validation in sklearn

Can grid-search-cross-validation be used to extract best parameters with Decision Tree classifier? http://scikit-learn.org/stable/tutorial/statistical_inference/model_selection.html

Upvotes: 6

Views: 23376

Answers (2)

Avinash Navlani
Avinash Navlani

Reputation: 525

Here is the code for decision tree Grid Search

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV

def dtree_grid_search(X,y,nfolds):
    #create a dictionary of all values we want to test
    param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}
    # decision tree model
    dtree_model=DecisionTreeClassifier()
    #use gridsearch to test all values
    dtree_gscv = GridSearchCV(dtree_model, param_grid, cv=nfolds)
    #fit model to data
    dtree_gscv.fit(X, y)
    return dtree_gscv.best_params_

Upvotes: 3

gowithefloww
gowithefloww

Reputation: 2251

Why not ?

I invite you to check documentation of GridsearchCV.

Example

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_auc_score

param_grid = {'max_depth': np.arange(3, 10)}

tree = GridSearchCV(DecisionTreeClassifier(), param_grid)

tree.fit(xtrain, ytrain)
tree_preds = tree.predict_proba(xtest)[:, 1]
tree_performance = roc_auc_score(ytest, tree_preds)

print 'DecisionTree: Area under the ROC curve = {}'.format(tree_performance)

And to extract the best parameters :

tree.best_params_
Out[1]: {'max_depth': 5}

Upvotes: 12

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