Random Forest Classification Algorithm Explain with Project
In this ML Algorithms course tutorial, we are going to learn “Random Forest Classification in detail. we covered it by practically and theoretical intuition.
- What is the Random Forest?
- What is Random Forest used for?
- How does Random Forest work?
- What is the Random Forest Classification?
- What is Gini impurity, entropy, the cost function for the CART algorithm?
- What is the Random Forest diagram?
- What is the difference between a decision tree and random forest?
- How to implement Random Forest Classification in python using sklearn?

Random Forest Classification Implementation
# -*- coding: utf-8 -*- """Random Forest Classification.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/13nr6Ix-AQ3B2vbcwr7E18kXalX_hTKYA ##Random Forest Classification ### Import Libraries """ # import libraries import numpy as np import pandas as pd """### Load Dataset""" #load dataset from sklearn.datasets import load_breast_cancer data = load_breast_cancer() data.data data.feature_names data.target data.target_names # create dtaframe df = pd.DataFrame(np.c_[data.data, data.target], columns=[list(data.feature_names)+['target']]) df.head() df.tail() df.shape """### Split Data""" X = df.iloc[:, 0:-1] y = df.iloc[:, -1] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2020) print('Shape of X_train = ', X_train.shape) print('Shape of y_train = ', y_train.shape) print('Shape of X_test = ', X_test.shape) print('Shape of y_test = ', y_test.shape) """## Train Random Forest Classification Model""" from sklearn.ensemble import RandomForestClassifier classifier = RandomForestClassifier(n_estimators=100, criterion='gini') classifier.fit(X_train, y_train) classifier.score(X_test, y_test) """## Predict Cancer""" patient1 = [17.99, 10.38, 122.8, 1001.0, 0.1184, 0.2776, 0.3001, 0.1471, 0.2419, 0.07871, 1.095, 0.9053, 8.589, 153.4, 0.006399, 0.04904, 0.05373, 0.01587, 0.03003, 0.006193, 25.38, 17.33, 184.6, 2019.0, 0.1622, 0.6656, 0.7119, 0.2654, 0.4601, 0.1189] patient1 = np.array([patient1]) patient1 classifier.predict(patient1) data.target_names pred = classifier.predict(patient1) if pred[0] == 0: print('Patient has Cancer (malignant tumor)') else: print('Patient has no Cancer (malignant benign)')