Random Forest mitigates overfitting and provides good generalization by combining predictions from multiple trees. For regression, it takes the average prediction from individual trees. It creates multiple decision trees during training, where each tree is trained on a random subset of features and data (bootstrap samples).įor classification, the final prediction is based on a majority vote from individual trees. Random Forest (RF):Random Forest is an ensemble learning method based on decision trees and is primarily used for classification and regression tasks.It is computationally expensive, especially for large datasets, as it requires calculating distances for all data points. KNN is non-parametric, meaning it doesn't make assumptions about the underlying data distribution. In regression, KNN takes the average or weighted average of the target values of the k-nearest neighbors as the predicted value for the query point. In classification, KNN assigns the majority class among the k-nearest neighbors as the predicted class for the query point. It works on the principle of finding the k-nearest data points to a given query point (new data), based on a distance metric (e.g., Euclidean distance). K-Nearest Neighbors (KNN):KNN is a simple and versatile algorithm used for both classification and regression tasks.Here's a brief differentiation of these algorithms:

K-Nearest Neighbors (KNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) are all popular machine learning algorithms, but they differ in their approach, strengths, and use cases.

Gradient boosting vs random forest