Random forest classifier - grid search

Tuning parameters in a machine learning model play a critical role. Here, we are showing a grid search example on how to tune a random forest model:

# Random Forest Classifier - Grid Search 
>>> from sklearn.pipeline import Pipeline 
>>> from sklearn.model_selection import train_test_split,GridSearchCV 
 
>>> pipeline = Pipeline([ ('clf',RandomForestClassifier(criterion='gini',class_weight = {0:0.3,1:0.7}))])

Tuning parameters are similar to random forest parameters apart from verifying all the combinations using the pipeline function. The number of combinations to be evaluated will be (3 x 3 x 2 x 2) *5 =36*5 = 180 combinations. Here 5 is used in the end, due to the cross-validation of five-fold:

>>> parameters = { 
...         'clf__n_estimators':(2000,3000,5000), 
...         'clf__max_depth':(5,15,30), 
...         'clf__min_samples_split':(2,3), 
...         'clf__min_samples_leaf':(1,2)  } 

>>> grid_search...