What is Random Forest? 🤔 (Machine Learning Explained)

Instead of trusting one decision tree, you ask hundreds of trees, then took the final answer by voting. That is random forest, one of the most reliable machine learning algorithm for tabular data. A decision tree learns by asking split questions like, >> [music] >> is age greater than 30? Is income above 10 lakhs? And keep splitting until it reaches a prediction. But, there is a problem. A single tree is easy to understand, but it can overfit. [music] It may memorize noise from the training data and fail on new data. Random forest fixes this by building many decision trees, not just one. Each tree is trained on a different random sample of the data, called bootstrapping. And at every split, it only looks at a random subset of features, not all of them. This randomness is the secret, because the trees are different, their mistakes [music] are different. So, when you combine them by majority voting for classification or average for regression, the model becomes more stable, more accurate, and less likely to overfit. It handles non-linear patterns, feature interaction, noisy data, and usually works very well even without heavy tuning. [music] But, it is not perfect. It is less interpretable than a single tree. It can be slower [music] with many trees, and feature importance can sometimes be misleading. So, in one [music] line, random forest is an ensemble of randomized decision trees that reduce variance and improve generalization by combining many weakly correlated models into one strong prediction. And like, share, and subscribe for more videos like this.