Understanding the Random Forest Algorithm in Machine Learning

The Random Forest Algorithm is a machine learning method that builds multiple decision trees to improve accuracy and reduce overfitting. For example, it’s used in healthcare to predict patient outcomes, aiding in decision-making. 

By aggregating results from various trees, it enhances stability and reliability for both classification and regression tasks.

This blog explores the Random Forest Algorithm, its key features, and its practical application using Python.

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What is the Random Forest Algorithm in Machine Learning?

The Random Forest Algorithm builds many decision trees on bootstrapped samples with random feature subsets. It reduces overfitting and improves accuracy in classification and regression tasks. 

Leo Breiman introduced it to fix the instability of single decision trees. The algorithm uses majority voting for classification and averaging for regression. 

For example, in churn prediction, each tree votes on whether a customer will churn or not, and the final output is based on the majority vote.

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Working of the Random Forest Algorithm in Machine Learning

Random Forest uses bootstrapped samples to train each decision tree on different data slices. At each split, it selects a random subset of features to reduce correlation between trees. 

For classification, it employs majority voting across trees. For regression, it averages the outputs to produce stable and accurate predictions.

The process can be understood in three steps: creating varied samples, selecting random features during splits, and combining outputs for final predictions.

1. Bootstrapping and Bagging in Random Forest

Bootstrapping generates varied samples, and bagging trains trees on these samples to reduce variance.

Each tree learns on a different dataset created by sampling with replacement. This ensures trees capture different patterns without relying on the same data points. Bagging averages or votes across outputs, reducing sensitivity to noise.

Example:
In fraud detection, each tree trains on a sample of transaction data, allowing the forest to capture different fraudulent patterns while smoothing out anomalies.

Also Read: Bagging vs Boosting in Machine Learning: Difference Between Bagging and Boosting

2. Random Feature Selection in Tree Splits

Random Forest selects a subset of features at each split to reduce correlation between trees.

This prevents all trees from using the same dominant features, encouraging diversity in decision boundaries. It improves generalization by exploring alternative feature interactions across trees.

Example:
In medical diagnosis with 50 features, each split may consider 7 random features, forcing trees to use different combinations when identifying disease indicators.

Also Read: Python Split() Function: Syntax, Parameters, Examples

3. Output Aggregation in Random Forest

Random Forest utilizes majority voting for classification and averaging for regression to combine the outputs of individual trees. Each tree makes its own prediction independently, and the forest combines these predictions to produce a final result. 

This approach helps to minimize overfitting and enhances the stability of predictions, even when some trees may be influenced by noise.

Example:
In predicting customer lifetime value, each tree predicts a numeric value, and the forest outputs the average, reducing the influence of outlier predictions from individual trees.

Also Read: Difference Between Anomaly Detection and Outlier Detection

After learning how Random Forest combines trees for stable predictions, it's crucial to explore the key features that make it effective for classification and regression tasks.

Key Features of the Random Forest Algorithm in Machine Learning

Random Forest improves prediction reliability by combining multiple decision trees while providing built-in tools for data interpretation and handling missing values. 

Random Forest is effective in predicting crop yield by analyzing soil quality and weather data. It can highlight the most significant environmental factors influencing the results, even when sensor readings are incomplete. Key features include: 

1. Ensemble Learning Benefits

Random Forest reduces overfitting and increases stability by combining predictions from many decision trees trained on different samples and feature subsets.

• Each tree learns different patterns, allowing the model to capture diverse data relationships.
• Aggregating outputs across trees reduces the impact of noisy or outlier data points.
• It maintains low bias while significantly lowering variance compared to single trees.
• Works well on high-dimensional datasets without heavy parameter tuning.
• Allows for the parallel training of trees, thereby improving scalability on large datasets.

Also Read: Top 15 Common Data Mining Algorithms Driving Business Growth!

2. Handling Missing Values

Random Forest can train and predict with missing data without requiring manual imputation or dropping rows.

• Uses available features for splitting decisions during tree construction.
• Allows the inclusion of incomplete data, preserving dataset size.
• Reduces preprocessing time in workflows with inconsistent data collection.
• Surrogate splits can guide splits when primary splitting features are missing.
• Helps in sensor-based and survey-based datasets where missing data is common.

Also Read: Data Preprocessing In Data Mining: Steps, Missing Value Imputation, Data Standardization

3. Feature Importance Estimation

Random Forest computes feature selection importance to indicate which features most influence predictions, aiding in interpretation and dimensionality reduction.

• Importance is calculated using impurity reduction across splits in all trees.
• Supports prioritization of high-impact features for focused data collection.
• Enables removal of low-importance features, reducing noise in the model.
• Assists in understanding model behavior and trust in predictions.
• Helpful in explaining results to stakeholders without requiring complex interpretation tools.

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4. Outlier Resistance

Random Forest is less sensitive to outliers due to its aggregation of multiple tree predictions.

• Outlier values influence only individual trees rather than the entire model.
• Helps maintain stable predictions even when extreme values are present.
• Reduces the need for heavy outlier preprocessing or removal.
• Useful in financial or environmental data where spikes and drops occur naturally.
• Contributes to consistent model performance across varying data conditions.

Also Read: Outlier Analysis in Data Mining: Techniques, Detection Methods, and Best Practices

5. Works with Large Feature Sets

Random Forest handles high-dimensional data efficiently without extensive feature engineering.

• Uses random feature subsets at each split, allowing trees to explore various feature combinations.
• Can process hundreds of features without significant drops in performance.
• Avoids overfitting on irrelevant features due to ensemble averaging.
• Useful for genomics, image data, or sensor-rich IoT datasets.
• Reduces the dependency on manual feature selection in early exploration stages.

Also Read: How to Choose a Feature Selection Method for Machine Learning

Let’s now learn how to implement and evaluate the Random Forest Algorithm in both classification and regression tasks.

Implementing and Evaluating the Random Forest Algorithm in Machine Learning

The Random Forest Algorithm works differently for classification and regression tasks. For classification, it uses majority voting across trees, while for regression, it averages the predictions. The algorithm relies on ensemble learning, aggregating outputs from multiple decision trees. 

In Python, RandomForestClassifier is used for classification, and RandomForestRegressor for regression. Performance is measured using accuracy for classification and RMSE or R² for regression.

Implementing the Random Forest Algorithm for Classification Tasks 

In this section, we use Random Forest for classification on the Wine dataset, which contains chemical measurements of wines. We split the data into training and testing sets, then train a Random Forest Classifier to learn from the data. The model’s performance is evaluated based on its accuracy. 

A confusion matrix for detailed class predictions, and ROC-AUC is used to assess class separation effectiveness.

Let’s implement this with Python:

Python Code

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score
from sklearn.preprocessing import label_binarize

# Load and split data
data = load_wine()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
rf_clf = RandomForestClassifier(n_estimators=100, random_state=42)
rf_clf.fit(X_train, y_train)

# Predictions
y_pred = rf_clf.predict(X_test)

# Evaluation
accuracy = accuracy_score(y_test, y_pred)
cm = confusion_matrix(y_test, y_pred)
y_test_binarized = label_binarize(y_test, classes=[0, 1, 2])
y_pred_proba = rf_clf.predict_proba(X_test)
roc_auc = roc_auc_score(y_test_binarized, y_pred_proba, average='macro', multi_class='ovr')

print("Accuracy:", accuracy)
print("Confusion Matrix:\n", cm)
print("ROC-AUC Score:", roc_auc)

Expected Output:

Accuracy: 1.0

Confusion Matrix:

 [[14  0  0]

  [ 0 14  0]

  [ 0  0  8]]

ROC-AUC Score: 1.0

Explanation:

• We use the Wine dataset, which has chemical measurements of different wines, to predict their type.
• We split the data to train the model on one part and test it on another to see how well it generalizes.
• Accuracy shows the percentage of correct predictions.
• The confusion matrix shows correct and incorrect predictions for each wine type.
• The ROC-AUC measures how well the model can distinguish between different wine types. 
• A score of 1.0 indicates perfect separation, which is achievable in this dataset due to the clear class differences.

Also Read: Top 32+ Python Libraries for Machine Learning Projects in 2025

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Implementing the Random Forest Algorithm for Regression Tasks 

In this section, we introduce how Random Forest can be used for regression tasks, which focus on predicting continuous numeric values instead of categories. Using the Diabetes dataset, we predict disease progression based on patient health data. 

We prepare and split the data, train the Random Forest Regressor to learn numeric patterns, and evaluate performance using RMSE, MAE, and R² to measure prediction accuracy.

Python Code:

from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import numpy as np

# Load and split data
data = load_diabetes()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
rf_reg = RandomForestRegressor(n_estimators=100, random_state=42)
rf_reg.fit(X_train, y_train)

# Predictions
y_pred = rf_reg.predict(X_test)

# Evaluation
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("RMSE:", rmse)
print("MAE:", mae)
print("R² Score:", r2)

Expected Output:

RMSE: 52.1

MAE: 40.1

R² Score: 0.42

Explanation:

• We utilize the Diabetes dataset to predict the progression of a disease using patient health data.
• The model is trained on a portion of the data and then tested on the remaining data to evaluate its ability to predict unseen values.
• RMSE tells us the average prediction error in the same units as the target (lower is better).
• MAE shows the average size of errors regardless of direction.
• R² indicates how much of the variation in the target variable the model explains, with 1 being perfect and values around 0.4-0.5 showing moderate predictive strength.

Also Read: Top 25+ Machine Learning Projects with Source Code To Excel in 2025

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Now, let’s look at the assumptions and advantages that make the Random Forest Algorithm a powerful tool in machine learning.

Assumptions and Advantages of the Random Forest Algorithm in Machine Learning

Random Forest requires specific conditions for effective and reliable performance. It assumes that data points are independent, that there are enough samples to support bootstrapping, and does not require feature scaling due to its tree-based nature.

Some key assumptions include. 

• Independence of Observations

  Random Forest assumes that each observation in the dataset is independent. Dependency between samples can reduce the effectiveness of bootstrapping, leading to correlated trees and reduced variance reduction. For time series or grouped data, dependencies should be addressed before using Random Forest to avoid biased predictions.

• Sufficiently Large Data Samples for Effective Bootstrapping

  Bootstrapping requires enough data to create diverse samples for each tree. When dealing with small datasets, bootstrapped samples can often overlap significantly. This overlap can lead to reduced diversity among trees, ultimately diminishing the advantages of ensemble learning algorithms

  Larger datasets help each tree learn different patterns, improving overall model robustness.

• No Requirement for Feature Scaling

  Random Forest does not require input features to be scaled. Since it uses decision trees internally, it relies on splitting data based on feature thresholds, which are unaffected by the scale or units of the features.

  This allows Random Forest to handle datasets with features of different units without additional data preprocessing.

Also Read: What is Bias in Data Mining? Types, Techniques, Strategies for 2025

Random Forest vs Decision Trees

While understanding these assumptions is important for correctly applying Random Forest, it's equally important to know why many practitioners move from using individual decision trees to Random Forests in real-world machine learning tasks. This is primarily for better accuracy, robustness, and generalization.

To understand why Random Forest is often preferred over traditional Decision Trees, let’s compare them across a few critical aspects. 

Also Read: Decision Tree vs Random Forest: Use Cases & Performance Metrics

Finally, let's explore how, despite its strengths, the Random Forest Algorithm introduces practical limitations that can affect its suitability depending on the use case.

Limitations of the Random Forest Algorithm in Machine Learning 

In high-frequency fraud detection systems that require rapid decisions, the slower inference speed of Random Forest can limit its deployment, despite its strong predictive power. Similarly, in medical diagnostics, where doctors require clear explanations for decisions, Random Forest’s low interpretability can be a barrier. 

The table below highlights these limitations, helping align the algorithm with operational constraints and interpretability needs in machine learning projects: 

Also Read: Top 10 Highest Paying Machine Learning Jobs in India [A Complete Report]

Now that you're clear on why Random Forest stands out, let’s explore how upGrad can support your next step in mastering machine learning.

How upGrad Can Help You in Your Machine Learning Journey? 

Random Forest is an ensemble algorithm that builds multiple decision trees, reduces overfitting, handles missing values, and ranks feature importance automatically. For instance, a marketing analyst utilized it to predict customer churn, enabling targeted retention efforts.

To learn Random Forest, start with Python and utilize libraries such as pandas, NumPy, and scikit-learn. Practice projects such as price prediction and email classification to build skills in data preparation, model training, and evaluation.

Moving from tutorials to real-world projects is a common hurdle. upGrad offers structured programs that combine theory, industry projects, mentorship, and career guidance to build confidence in ML.

Some additional courses include: 

• DBA in Emerging Technologies with Concentration in Generative AI
• Executive Diploma in Data Science & AI
• Post Graduate Certificate in Data Science & AI (Executive)

With personalized mentorship, you receive clear and actionable feedback. Offline centres offer peer learning, in-person workshops, and faculty support to help maintain consistent progress.

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Reference:
https://pmc.ncbi.nlm.nih.gov/articles/PMC9226542