Random Forest Classifier using Scikit-learn

Random Forest is a popular ensemble learning method that can be used for classification and regression tasks. It builds multiple decision trees and aggregates their predictions to improve accuracy and reduce overfitting. Scikit-learn is a popular Python library for machine learning, including Random Forest Classifier.

Random Forest Classifier

Here’s an example of how to use Random Forest Classifier in Scikit-learn:

# importing required libraries 
# importing Scikit-learn library and datasets package
from sklearn import datasets  
  
# Loading the iris plants dataset (classification)
iris = datasets.load_iris()    

print(iris.target_names)
print(iris.feature_names)

['setosa' 'versicolor' 'virginica']
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

# dividing the datasets into two parts i.e. training datasets and test datasets
X, y = datasets.load_iris( return_X_y = True)

# Splitting arrays or matrices into random train and test subsets
from sklearn.model_selection import train_test_split
# i.e. 70 % training dataset and 30 % test datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30)

# importing random forest classifier from assemble module
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
# creating dataframe of IRIS dataset
data = pd.DataFrame({"sepallength": iris.data[:, 0], "sepalwidth": iris.data[:, 1],
     "petallength": iris.data[:, 2], "petalwidth": iris.data[:, 3],
     "species": iris.target})

# printing the top 5 datasets in iris dataset
print(data.head())

 sepallength  sepalwidth  petallength  petalwidth  species
0          5.1         3.5          1.4         0.2        0
1          4.9         3.0          1.4         0.2        0
2          4.7         3.2          1.3         0.2        0
3          4.6         3.1          1.5         0.2        0
4          5.0         3.6          1.4         0.2        0

# creating a RF classifier
clf = RandomForestClassifier(n_estimators = 100)

# Training the model on the training dataset
# fit function is used to train the model using the training sets as parameters
clf.fit(X_train, y_train)

# performing predictions on the test…