we can change the values of features in prediction to test it random forest classifier as we know that ensemble methods are the methods which combine machine learning models into more powerful machine learning model random foresta collection of decision treesis one of them it is better than single decision tree because while retaining the predictive powers it can reduce over-fitting by averaging the results herewe are going to implement the random forest model on scikit learn cancer dataset import the necessary packagesfrom sklearn ensemble import randomforestclassifier from sklearn model_selection import train_test_split from sklearn datasets import load_breast_cancer cancer load_breast_cancer(import matplotlib pyplot as plt import numpy as np nowwe need to provide the dataset which can be done as followscancer load_breast_cancer(x_trainx_testy_trainy_test train_test_split(cancer datacancer targetrandom_state= after providing the datasetwe need to fit the model which can be done as followsforest randomforestclassifier(n_estimators= random_state= forest fit(x_train,y_trainnowget the accuracy on training as well as testing subsetif we will increase the number of estimators thenthe accuracy of testing subset would also be increased print('accuracy on the training subset:( )',format(forest score(x_train,y_train))print('accuracy on the training subset:( )',format(forest score(x_test,y_test))output accuracy on the training subset:( accuracy on the training subset:( nowlike the decision treerandom forest has the feature_importance module which will provide better view of feature weight than decision tree it can be plot and visualize as follows