using best params from gridsearchcv

Question

I don't know if it is the right question to ask here, but I will ask anyways. If it is not allowed please do let me know.

I have used GridSearchCV to tune parameters to find best accuracy. This is what I have done:

from sklearn.grid_search import GridSearchCV
parameters = {'min_samples_split':np.arange(2, 80), 'max_depth': np.arange(2,10), 'criterion':['gini', 'entropy']}
clfr = DecisionTreeClassifier()
grid = GridSearchCV(clfr, parameters,scoring='accuracy', cv=8)
grid.fit(X_train,y_train)
print('The parameters combination that would give best accuracy is : ')
print(grid.best_params_)
print('The best accuracy achieved after parameter tuning via grid search is : ', grid.best_score_)

This gives me following result:

The parameters combination that would give best accuracy is : 
{'max_depth': 5, 'criterion': 'entropy', 'min_samples_split': 2}
The best accuracy achieved after parameter tuning via grid search is :  0.8147086914995224

Now, I want to use these parameters while calling a function that visualizes a decision tree

The function looks something like this

def visualize_decision_tree(decision_tree, feature, target):
    dot_data = export_graphviz(decision_tree, out_file=None, 
                         feature_names=feature,  
                         class_names=target,  
                         filled=True, rounded=True,  
                         special_characters=True)  
    graph = pydotplus.graph_from_dot_data(dot_data)  
    return Image(graph.create_png())

Right now I am trying to use the best parameters provided by GridSearchCV to call the function in the following way

dtBestScore = DecisionTreeClassifier(parameters = grid.best_params_)
dtBestScore = dtBestScore.fit(X=dfWithTrainFeatures, y= dfWithTestFeature)
visualize_decision_tree(dtBestScore, list(dfCopy.columns.delete(0).values), 'survived')

I am getting error in first line of code which says

TypeError: __init__() got an unexpected keyword argument 'parameters'

Is there some way I can somehow manage to use the best parameters provided by grid search and use it automatically? Rather than looking the result and manually setting value of each parameter?

Answer 1

Try python kwargs:

DecisionTreeClassifier(**grid.best_params)

See http://pythontips.com/2013/08/04/args-and-kwargs-in-python-explaine‌​d for more on kwargs.