Random Forests for Regression Problems: Machine Learning Beginner Guide

Learn an ensemble learning technique that combines decision trees to make predictions

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Random forests are a popular ensemble learning technique that can be used for both classification and regression problems. Let’s focus on using random forests for regression problems.

What is a Random Forest?

A random forest is an ensemble learning technique that combines multiple decision trees to make predictions. Each decision tree is built on a random subset of the data and a random subset of the features, which helps to reduce overfitting and improve the model’s generalization ability.

Let’s look at the step-by-step guide to building a Random Forest for Regression:

1. Import the necessary libraries.

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import mean_squared_error, r2_score

2. Load the data and split it into training and testing sets.

data = pd.read_csv("data.csv")
X = data.drop("target"…