These are bootstrap aggregators where equal voting rights have been assigned to every model. The variance of these aggregators is maintained by drawing a random subset when making a decision.

Random forests are extensions of the bagging method. A random forest is a collection of decision trees that help in classification, regression, and decisions. The following shows the code for importing RandomForestClassifier: 

import pandas
from sklearn import model_selection
from sklearn.ensemble import RandomForestClassifier
get_values = new_count_df.values
A =get_values[:,0:8]
B =get_values[:,8]
seed = 7
number_of_trees = 50
max_num_features = 2
kfold_crossval = model_selection.KFold(n_splits=10, random_state=seed)
model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features)
results = model_selection.cross_val_score(model, A, B, cv=kfold_crossval)
print(results.mean())