Ensemble Learning

Ensemble Learning

Ensemble learning is a machine learning approach that combines the predictions of multiple individual models to produce a single, typically more accurate and r…

Random Forest

Random Forest is an ensemble learning algorithm that builds many decision trees on randomly resampled data and random feature subsets, then aggregates their pr…

Gradient Boosting

Gradient Boosting is an ensemble learning technique that builds a model as a sequence of weak learners, typically shallow decision trees, where each new learne…

XGBoost

XGBoost (Extreme Gradient Boosting) is an open-source, highly optimized implementation of the gradient boosting algorithm, widely used for structured/tabular d…

Support Vector Machine

A Support Vector Machine (SVM) is a supervised learning algorithm that finds the hyperplane which best separates classes of data by maximizing the margin betwe…

K-Means Clustering

K-Means is an unsupervised machine learning algorithm that partitions a dataset into K clusters by iteratively assigning points to the nearest cluster centroid…

Decision Tree

A decision tree is a supervised learning model that predicts an outcome by learning a hierarchy of simple, interpretable if-then decision rules based on input…

Stacking (ML)

Stacking, or stacked generalization, is an ensemble learning technique that trains a meta-model to combine the predictions of several diverse base models, lear…