lazybrains 1.0.5

Model Selection Tool

🧠 Universal ML Model Explorer Pro

One-line ML pipeline that preprocesses, trains, compares, and visualizes the best model — automatically.

Automatically train, evaluate, compare, and visualize multiple machine learning models — all with one command.

🚀 Features

• Auto detection: Classification or Regression
• Auto preprocessing: Scaling, Encoding, Imputation, PCA
• Parallel model training on all cores
• SHAP interpretability plots
• Beautiful visual reports (Confusion Matrix, ROC, Residuals, etc.)
• CLI + Notebook compatible

📦 Installation

pip install -r requirements.txt

🧪 CLI Usage

python main.py path/to/dataset.csv target_column_name

Optional flags:

• --output_dir: Folder to save results (default: results)
• --pca_components: Apply PCA on numeric features
• --no_shap: Disable SHAP plot (faster)

🧬 Python Usage

from yourlib import run_pipeline_in_notebook

run_pipeline_in_notebook(
    dataset_path="data.csv",
    target_column="target",
    pca_components=5,
    no_shap=False
)

📂 Output

• best_model.pkl: Trained model
• Plots: Confusion Matrix, ROC, Residuals, SHAP
• model_report.txt: Full model comparison

🛠️ Supported Models

• Linear, Tree-based, Ensemble (RF, GB, AdaBoost, XGBoost), KNN, SVM, Stacking
• Auto selection of best based on Accuracy / R²

Run this in your terminal to install all dependencies

pip install pandas numpy matplotlib seaborn scikit-learn xgboost shap joblib rich

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