Machine Learning Models

A gradient boosting algorithm that natively handles categorical features without preprocessing. Uses ordered boosting and innovative techniques to combat prediction shift, providing excellent results with default parameters.

A supervised learning algorithm that creates a flowchart-like tree structure based on feature importance to make decisions and predictions. It recursively splits data into subsets based on the most discriminative features, making it valuable for classification, regression, feature selection, and creating transparent models with clear decision rules that are easily interpretable.

A powerful ensemble technique that builds models sequentially, with each new model correcting errors made by previous ones. It combines weak learners (typically decision trees) into a strong predictive model by optimizing a differentiable loss function through gradient descent, making it one of the most effective methods for structured data prediction tasks in competitions, business applications, and scientific research. The method is particularly effective for handling heterogeneous features, missing values, and complex non-linear relationships.

A simple yet effective non-parametric method that classifies or predicts based on the majority class or average value of the k nearest data points. KNN makes no assumptions about data distribution and adapts to complex decision boundaries through local approximations, making it useful for classification, regression, recommendation systems, and as a baseline for more complex models when prior knowledge about data structure is limited. The algorithm's performance heavily depends on distance metric selection and feature scaling.

A gradient boosting framework that uses tree-based learning algorithms, optimized for distributed and efficient training with lower memory usage. Uses leaf-wise tree growth with depth limits for better accuracy.

A probabilistic classifier based on Bayes' theorem with an assumption of conditional independence between features. It calculates the probability of each class given the feature values and selects the most likely class, making it computationally efficient for high-dimensional data. Naive Bayes is particularly effective for text classification (spam detection, sentiment analysis), medical diagnosis, and recommendation systems when features can be reasonably assumed to be independent, despite often violating this assumption in practice while still performing well.

Computational models inspired by the human brain's structure, consisting of interconnected nodes (neurons) organized in layers that process information hierarchically. They learn complex nonlinear relationships through backpropagation and gradient descent optimization (e.g., Adam, SGD), enabling state-of-the-art performance in tasks like image recognition, natural language processing, and time-series forecasting. Supports automatic feature extraction from raw data.

An ensemble learning method for classification and regression that constructs multiple decision trees.

A supervised learning algorithm that finds the optimal hyperplane to separate classes in feature space, potentially after mapping data to higher dimensions using kernel functions. SVMs maximize the margin between different classes while handling nonlinear relationships through kernel tricks, making them effective for classification (SVC), regression (SVR), outlier detection, and applications with clear margin of separation in high-dimensional spaces. They are particularly robust against overfitting in high-dimensional spaces and effective when the number of dimensions exceeds the number of samples.

A scalable, distributed gradient-boosted decision tree (GBDT) machine learning library that provides parallel tree boosting. Known for its speed, performance, and regularization techniques to prevent overfitting.