MIScikit-Learn 0.1.0

A lightweight machine learning library inspired by scikit-learn.

Lightweight Machine Learning Library

Overview

This project aims to create a lightweight machine learning library inspired by Scikit-Learn. It provides powerful, modular, and easy-to-use tools for building machine learning models and analyzing data.

Modules Description

1. Preprocessing Module

Handles all necessary steps to prepare data before usage in machine learning models.

• Data Cleaning: Handle missing values, remove outliers, correct inconsistencies.
• Feature Scaling: Standardize and normalize features.
• Categorical Encoding: Convert categorical data to numerical format.
• Feature Selection: Select important features for model performance.

2. Supervised Learning Module

Includes algorithms for training and making predictions with labeled data.

• Regression Algorithms: Linear regression, logistic regression, etc.
• Classification Algorithms: K-nearest neighbors, decision trees, support vector machines.
• Model Training: Methods to fit models to training data.
• Prediction: Methods to make predictions on new data.

3. Model Selection and Evaluation Module

Tools to select the best model and tune its parameters, along with evaluating its performance.

• Cross-validation: Split data to validate model performance.
• Grid Search: Search across parameters to find optimal settings.
• Performance Metrics: Accuracy, precision, recall, F1-score, ROC curve, MSE, RMSE.

4. Ensemble Methods Module

Techniques that combine the predictions of multiple machine learning algorithms.

• Bagging: Reduce model variance.
• Boosting: Reduce both bias and variance.
• Stacking: Combine outputs of multiple models to improve performance.

5. Neural Networks Module

Handles basic neural network architectures.

• Feedforward Neural Networks: Basic layers for feedforward architectures.
• Training: Train neural networks with backpropagation.
• Activation Functions: Sigmoid, ReLU, softmax.

6. Utilities Module

Supports other modules by providing common functionalities and integrations.

• Data Loaders: Load and preprocess data.
• Visualization Tools: Visualize data distributions and model performance.
• Helper Functions: Assist in model training and data manipulation.

7. Meta Estimators Module

Extends the functionality of basic estimators by combining or enhancing them.

• Pipelines: Chain multiple steps into a unified model.
• Model Selection Tools: Parameter tuning to find best model configuration.
• Ensemble Methods: Advanced stacking generalization.

8. Transformer Mixins Module

Alters or augments data before it reaches an estimator.

• Fit and Transform: Methods to fit to data, then transform it.
• Fit-Predict Shortcuts: Efficient methods combining fitting and transforming.