Modules and Functions

The aolearn library is a collection of machine learning algorithms implemented in a modular fashion. Each module focuses on a specific type of machine learning technique, and each function within a module serves a distinct purpose—training models, making predictions, and handling classification tasks.

# Modules Overview

1. Linear Regression (aolearn.linear_regression)

   • Implements simple linear regression using gradient descent for parameter optimization.

2. Logistic Regression (aolearn.logistic)

   • Handles binary classification problems using logistic regression and the sigmoid function.

3. Lasso Regression (aolearn.lasso)

   • Introduces L1 regularization to reduce overfitting and enforce sparsity in the model.

4. Ridge Regression (aolearn.ridge)

   • Implements L2 regularization to reduce overfitting while keeping all features.

5. Multiclass Logistic Regression (aolearn.multiclass_logistic)

   • Extends logistic regression to handle multiple classes using the softmax function.

6. Naive Bayes (aolearn.naive_bayes)

   • A probabilistic classification method based on Bayes' theorem, assuming feature independence.

7. k-Nearest Neighbors (aolearn.knn)

   • A non-parametric algorithm that classifies data points based on proximity to labeled examples.

8. Clustering (aolearn.clustering)

   • Implements k-Means clustering for unsupervised learning and pattern discovery.

Functions Overview

Each module is stored in a separate file within the aolearn package. These files contain:

• Function Definitions: Implementation of training and prediction functions.
• Parameter Descriptions: Explanation of inputs and outputs for each function.
• Mathematical Concepts: Descriptions of the algorithms and optimization techniques used.