Random Forest is a popular machine learning algorithm that builds multiple decision trees and combines their outputs to arrive at one final prediction. Think of it as asking a group of experts for their opinion and then going with the majority vote. This ensemble approach makes the algorithm robust and versatile.
How Does It Work?
Instead of relying on a single decision tree which can be prone to bias or overfitting, Random Forest constructs many trees by:
Using Random Samples of Data: Each tree is trained on a different subset of the dataset.
Employing Feature Randomness: Only a random subset of features is considered when splitting nodes in each tree.
Once all trees are built, Random Forest aggregates their results. For classification tasks, it takes a majority vote, and for regression tasks, it averages the outcomes. This process leads to more accurate and reliable predictions.
Why Does It Reduce Overfitting?
Overfitting happens when a model learns the noise in the training data instead of the actual underlying pattern. Random Forest combats this issue through:
Bagging (Bootstrap Aggregation): Training each decision tree on a different random subset of the data.
Feature Randomness: Limiting the number of features available for splitting at each node, ensuring that the trees are less correlated.
By reducing the correlation among individual trees, Random Forest minimizes the risk of overfitting, leading to a model that generalizes better to unseen data.
Applications
Random Forest is not limited to a single type of problem. It can be effectively applied in:
Classification Tasks: For example, determining whether a credit application should be approved (Yes/No).
Regression Tasks: Such as predicting continuous outcomes like house prices.
Its ability to handle both classification and regression tasks makes it a powerful tool in a data scientist's toolkit.
I’m presenting a visual representation of a Random Forest Classifier (e.g. four decision trees)
Liked this article? Make sure to 💜 click the like button.
Feedback or addition? Make sure to 💬 comment.
Know someone that would find this helpful? Make sure to 🔁 share this post.
Get in touch
You can find me on LinkedIn | YouTube | GitHub | X
Book an Appointment: Topmate
If you wish to make a request on particular topic you would like to read, you can send me an email to analyticalrohit.connect@gmail.com
Share this post