Clever Geek Handbook

Grid-search-cross-validation in sklearn

Can I use grid cross validation to extract the best parameters with a decision tree classifier? http://scikit-learn.org/stable/tutorial/statistical_inference/model_selection.html

+5


source to share


3 answers


Why not?

I invite you to check the GridsearchCV documentation .

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}
+8


source


This answer is great.

However, I would like to point out a couple of links where it explains how to generalize very well the selection of the best options and with examples:



0


source


Here is the code for the Grid Search decision tree

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_
0


source






More articles:


All Articles