Algorithm Roadmap¶

This page tracks the implementation status of algorithms in lostml.

Implemented Algorithms ✅¶

✅ Linear Regression - Basic linear regression using gradient descent
✅ Ridge Regression - Linear regression with L2 regularization
✅ Lasso Regression - Linear regression with L1 regularization (feature selection)
✅ Elastic Net - Combines L1 and L2 regularization

✅ Logistic Regression - Binary classification using sigmoid function
✅ K-Nearest Neighbors (KNN) - Instance-based classification with distance metrics

✅ Decision Tree - Classification and regression using recursive splitting - Status: Implemented - Use Cases: Interpretable models, feature importance, non-linear relationships - Features: Supports both Gini (classification) and MSE (regression) criteria
✅ Random Forest - Ensemble of decision trees with bootstrap aggregating - Status: Implemented - Use Cases: General-purpose classification and regression, handles overfitting well - Features: Bootstrap sampling, random feature selection, majority voting (classification), averaging (regression)

⏳ K-Means Clustering - Partition data into k clusters - Status: Planned - Priority: High - Use Cases: Customer segmentation, data exploration, pattern discovery
⏳ PCA (Principal Component Analysis) - Dimensionality reduction - Status: Planned - Priority: High - Use Cases: Feature reduction, visualization, noise reduction

⏳ Naive Bayes - Probabilistic classifier - Status: Planned - Priority: Medium - Use Cases: Text classification, spam detection, fast classification
⏳ Support Vector Machine (SVM) - Maximum margin classifier - Status: Planned - Priority: Medium - Use Cases: Classification with clear margins, non-linear data (with kernels)

Current Progress: 8/12 algorithms implemented (67%)

Next Up: K-Means → PCA → Naive Bayes