Random forest classifier in ML Python

Introduction

A random forest classifier helps us make better decisions by combining many simple decision trees. It reduces mistakes by averaging their answers.

When you want to classify emails as spam or not spam.

When predicting if a patient has a disease based on medical data.

When sorting images into categories like cats, dogs, or birds.

When deciding if a loan application should be approved or denied.

When you want a model that works well without much tuning.

Syntax

ML Python

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

n_estimators sets how many trees the forest has.

random_state makes results repeatable by fixing randomness.

Examples

This creates a forest with 50 trees and trains it on data.

ML Python

model = RandomForestClassifier(n_estimators=50)
model.fit(X_train, y_train)

This limits each tree to 5 levels deep to avoid overfitting.

ML Python

model = RandomForestClassifier(n_estimators=200, max_depth=5)
model.fit(X_train, y_train)

This gets the predicted classes for new data.

ML Python

predictions = model.predict(X_test)

Sample Program

This program trains a random forest on the iris flower data and shows how well it predicts the flower types.

ML Python

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load example data
iris = load_iris()
X, y = iris.data, iris.target

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create and train the random forest model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predict on test data
predictions = model.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2f}")

Important Notes

Random forests work well even if some trees make mistakes because they vote together.

More trees usually improve accuracy but take longer to train.

Setting random_state helps you get the same results every time you run the code.

Summary

Random forest combines many decision trees to improve prediction.

It is easy to use and works well on many problems.

Adjusting the number of trees and tree depth helps control performance.