Decision Tree

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…

Naive Bayes Classifier

A Naive Bayes classifier is a probabilistic machine learning algorithm based on Bayes' theorem that predicts a class by assuming all input features are conditi…

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…

K-Nearest Neighbors

K-Nearest Neighbors (KNN) is a simple, non-parametric supervised learning algorithm that classifies or predicts a new data point based on the majority label or…

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…

Boosting

Boosting is an ensemble learning technique that builds a sequence of models, where each new model is trained to correct the errors made by the combined ensembl…