MLBuddy 0.1.0

A simple AutoML library

MLBuddy

MLBuddy is a simple yet powerful machine learning automation library that helps you quickly build, evaluate, and compare multiple machine learning models with minimal code.

Features

• Automatic preprocessing of data
• Support for both classification and regression tasks
• Built-in model selection with optimized hyperparameters
• Model performance comparison through a leaderboard
• Easy evaluation on test data
• Ensemble models for improved performance

Quick Start

import pandas as pd
from MLBuddy import MLBuddy
from sklearn.model_selection import train_test_split

# Load your dataset
df = pd.read_csv('your_dataset.csv')

# Split into train and test sets
train, test = train_test_split(df, test_size=0.2, random_state=42)

# Initialize MLBuddy with your target column and task type
predictor = MLBuddy(label='target_column', task_type='classification')  # or 'regression'

# Fit models on training data
predictor.fit(train)

# View model performance leaderboard
print(predictor.leaderboard())

# Evaluate the best model on test data
test_performance = predictor.evaluate(test)
print(test_performance)

Supported Models

Classification

• Logistic Regression
• Naive Bayes
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)
• Decision Tree
• Random Forest
• Gradient Boosting
• XGBoost
• LightGBM
• AdaBoost
• Ensemble models (stacking and voting)

Regression

• Linear Regression
• Ridge Regression
• Lasso Regression
• Elastic Net
• Polynomial Regression
• Support Vector Regression (SVR)
• Decision Tree
• Random Forest
• Gradient Boosting
• XGBoost
• LightGBM
• Ensemble models (stacking and voting)

Advanced Usage

Custom Preprocessing

MLBuddy automatically handles:

• Missing value imputation
• Categorical encoding
• Feature scaling
• Feature selection

Performance Metrics Classification

• Accuracy
• Precision
• Recall
• F1 Score Regression
• R² Score

License

This project is licensed under the MIT License - see the LICENSE file for details.