Dod-o/Statistical-Learning-Method_Code
Statistical Learning Method Code
This project is a reference collection of statistical learning algorithms built from scratch using NumPy for linear algebra and matrix operations. It serves as an educational resource for studying the mathematical foundations and inner workings of machine learning models through manual implementations.
The codebase provides hand-coded implementations of both supervised and unsupervised learning. This includes classification and regression models such as support vector machines, decision trees, and Naive Bayes, as well as data clustering and pattern discovery methods like k-means and hierarchical clustering.
The project translates academic pseudocode and mathematical formulas into Python logic, utilizing NumPy vectorization for matrix-based calculations. The implementations employ class-based encapsulation and iterative parameter optimization to achieve model fitting and convergence.
Features
- Machine Learning Education - Learning the mathematical foundations of statistical learning by building core algorithms from scratch without using high-level libraries.
- Machine Learning Implementations - A collection of statistical learning algorithms built from scratch using NumPy for linear algebra and matrix operations.
- Supervised Learning - Provides hand-coded implementations of supervised learning algorithms including support vector machines, decision trees, and Naive Bayes.
- Algorithm Implementations - Translates mathematical formulas and academic pseudocode directly into Python logic without using high-level machine learning libraries.
- Educational Implementations - The project translates academic pseudocode and mathematical formulas into functional Python code to teach the foundations of machine learning.
- Algorithm Logic References - The project serves as a reference codebase for studying the inner workings and parameter optimization of statistical learning models.
- Clustering Algorithms - The project provides manual implementations of data grouping methods, specifically k-means and hierarchical clustering.
- Iterative Parameter Optimizations - Updates model weights through repeated cycles using loss functions and gradient descent until a stability criterion is met.
- Logistic Regression Models - Provides a manual implementation of logistic regression for binary and multiclass classification tasks.
- Supervised Learning Models - Building classification and regression models by manually coding the logic for algorithms like support vector machines and decision trees.
- Dataset-Driven Training - The project implements parameter adjustment processes that process training datasets until a specific convergence criterion is met.
- Perceptrons - Implements the core mathematical logic for perceptrons and k-nearest neighbor classification.
- Unsupervised Learning - Implements core algorithms for discovering patterns and structures in unlabeled datasets, including k-means and hierarchical clustering.
- Clustering and Density Estimation - Developing data clustering and pattern discovery tools by manually implementing methods such as k-means and hierarchical clustering.
- Vectorized Array Operations - Uses array-based linear algebra operations to perform high-performance mathematical calculations instead of manual Python loops.
- Linear Algebra Routines - The project utilizes NumPy arrays to perform multi-dimensional data transformations required for statistical learning.
- Algorithm Development - Writing high-performance linear algebra and matrix operations using NumPy to implement mathematical formulas and academic pseudocode.
- Vectorized Operations - The project employs NumPy vectorization to perform high-performance linear algebra calculations instead of manual Python loops.
- Algorithm Logic Implementations - The project implements decision trees and clustering logic using standard Python control structures to mirror mathematical pseudocode.
- Convergence Detection Methods - Adjusts internal model parameters until a specific mathematical threshold or error margin is reached during the fitting process.
- Machine Learning Education - Manual implementations of classic machine learning algorithms for studying the mathematical foundations and inner workings of models.
- Logic Flow Demonstrations - Translates mathematical formulas and academic pseudocode directly into standard Python control structures and operational logic.
