Random forests are ensemble learning methods that are used for either classification or regression purposes. Random forests are composed of several decision trees that are combined together to make a unanimous decision or classification. Random forest are better than just regular decision trees because they do not cause overfitting of the data:

We would then try to use the random forest classifier:

from sklearn.ensemble import RandomForestClassifier
 rf = RandomForestClassifier()
scores = cross_val_score(rf, X, y, scoring='accuracy', cv=5)
print np.mean(scores)  
# nicer
rf.fit(X, y)

The output can be seen as follows:

Out[39]:
0.9997307783262454

With random forest, we are able to get more best term to use than a single decision tree:

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)
 pd.DataFrame({'feature':X.columns, 'importance':rf.feature_importances_}).sort_values('importance', ascending=False).head(10)

The features importance can be seen here: