mlforgex 1.0.7

Lightweight ML utility for automated training, evaluation, and prediction with CLI and Python API support

mlforgex

mlforgex is a Python package that enables easy training, evaluation, and prediction for machine learning models on cleaned dataset. It supports both classification and regression problems, automates preprocessing, model selection, hyperparameter tuning, and generates useful artifacts and plots for analysis.

Features

• Automatic data preprocessing (missing value handling, encoding, scaling)
• Imbalance handling (under-sampling, over-sampling)
• Model selection and evaluation (classification & regression)
• Hyperparameter tuning with RandomizedSearchCV
• Artifact saving (model, preprocessor, encoder)
• Visualization of metrics and learning curves
• Simple CLI for training and prediction

Installation

Install mlforge using pip:

pip install mlforgex

stall .

## Requirements

- Python >= 3.8
- pandas
- numpy
- scikit-learn
- seaborn
- matplotlib
- xgboost
- imbalanced-learn

See [requirements.txt](requirements.txt) for details.

## Usage

### Train a Model

You can train a model using the CLI:

```sh
mlforge-train --data_path path/to/your/data.csv --dependent_feature TargetColumn --rmse_prob 0.3 --f1_prob 0.7 --n_jobs -1 --n_iter 100 --cv 3

Or programmatically:

from mlforge import train_model

result = train_model(
    data_path=<data_path>,
    dependent_feature=<dependent_feature>,
    rmse_prob=<rmse_probability>,
    f1_prob=<f1_probability>,
    n_jobs=<n_jobs>
)
print(result)

Predict

Use the CLI:

mlforge-predict --model_path path/to/model.pkl --preprocessor_path path/to/preprocessor.pkl --input_data path/to/input.csv --encoder_path path/to/encoder.pkl

Or programmatically:

from mlforge import predict

result = predict(
    <model.pkl>,
    <preprocessor.pkl>,
    <input_data.csv>,
    <encoder.pkl>
)
print(result)

Artifacts

After training, the following files are saved :

• model.pkl: Trained model
• preprocessor.pkl: Preprocessing pipeline
• encoder.pkl: Label encoder (for classification)
• Plots/: Visualizations (correlation heatmap, confusion matrix, ROC curve, etc.)

Testing

Run tests using pytest:

pytest test/

Author

Priyanshu Mathur
Portfolio
Email: [e-mail]

Project Links

• PyPI