Implement comprehensive ML library from scratch with 12 algorithms, utilities, and examples

[Copilot]

Implements a complete machine learning library from theoretical foundations for the MAT 3533 course, covering probability-based methods through deep neural networks with full preprocessing, evaluation, and visualization pipeline.

Algorithms (12 total)

Supervised Learning (7)

• Naive Bayes (Gaussian), Logistic Regression (L1/L2), Linear Regression (Normal Equation + Gradient Descent)
• K-Nearest Neighbors, Decision Trees (CART), Random Forests, SVM

Unsupervised Learning (5)

• K-Means, DBSCAN, Hierarchical Clustering
• PCA, t-SNE

Neural Networks

• Feedforward networks with backpropagation
• Dense/Activation layers, 4 activation functions (Sigmoid, ReLU, Tanh, Softmax)
• MSE and Cross-Entropy loss

Utilities

Preprocessing: StandardScaler, MinMaxScaler, LabelEncoder, OneHotEncoder, train_test_split

Evaluation: Accuracy, Precision, Recall, F1, Confusion Matrix, MSE, MAE, R²

Model Selection: K-Fold CV, GridSearchCV, RandomizedSearchCV

Visualization: 11 plotting functions (decision boundaries, confusion matrices, learning curves, clustering, PCA variance, ROC curves)

Examples

7 comprehensive examples demonstrating:

1. Multi-algorithm classification comparison
2. Regression with regularization
3. Clustering techniques with elbow method
4. Dimensionality reduction and variance analysis
5. Neural network training and evaluation
6. Hyperparameter tuning with cross-validation
7. End-to-end ML pipeline

Usage

from algorithms.supervised import LogisticRegression, RandomForestClassifier
from utils.preprocessing import StandardScaler, train_test_split
from utils.evaluation import accuracy_score
from utils.model_selection import GridSearchCV

# Train and evaluate
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)

model = LogisticRegression(learning_rate=0.1, n_iterations=1000, regularization='l2')
model.fit(X_train, y_train)
print(f"Accuracy: {accuracy_score(y_test, model.predict(scaler.transform(X_test)))}")

# Hyperparameter tuning
rf = RandomForestClassifier()
grid = GridSearchCV(rf, {'n_trees': [20, 50], 'max_depth': [5, 10]}, cv=5)
grid.fit(X_train, y_train)
print(f"Best params: {grid.best_params}, Score: {grid.best_score}")

Run examples: python run_example.py <1-7> or test suite: python test_implementations.py

Stats: 33 Python files, 4,622 lines of code, all algorithms implemented from scratch using NumPy with mathematical foundations documented in docstrings.

Original prompt

Comprehensive implementation of fundamental machine learning algorithms from probability-based methods to deep neural networks.

• Implementation of ML algorithms from theoretical foundations
• Data preprocessing and feature engineering techniques
• Model selection and hyperparameter optimization
• Performance evaluation and interpretation
• Visualization of complex datasets and results
• Understanding trade-offs between different algorithms

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