Random Forest algorithm is like an ensemble algorithm made of Decision Trees, which comprises more than one decision tree to create a model. It creates more than one tree like conditional control statements to create its model hence it is named as Random Forest.
Random Forest machine learning algorithm can be used to solve both regression and classification problem.
In this post we will be implementing a simple Random Forest classification model using python and sklearn.
First thing first , let us import the required libraries.
import numpy as np import pandas as pd import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn import metrics
After that we need to load data in jupyter notebook. You can find the data here.
Note that the above data has features called x1 and x2 and a label called label. The next step would be to split data into features and label as well as train and test as below.
x= df.drop('label',axis = 1) y= df.label x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42)
After this let us train the model
RandomForestClfModel = RandomForestClassifier()
Then we need to train the model
This is the time to do some prediction
y_pred = RandomForestClfModel.predict(x_test)
After the prediction is done we can evaluate the model by calculating accuracy as below.
accuracy = metrics.accuracy_score(y_test, y_pred)
I hope you enjoyed this article and can start using some of the techniques described here in your own projects soon. Cheers !!