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)')