Machine Learning Modelling for Everyone
Create ML Model
Fine Tune ML Model
Pipe operator
Plotting Calibration Curve
Plotting Confusion Matrix
Plotting Output Distribution By Class
Plotting Gain Curve
Plot Neural Network Architecture
Plotting Integrated Gradients Plots
Plotting Lift Curve
Plot Neural Network Loss Curve
Plotting Olden Values Barplot
Plotting Permutation Feature Importance Barplot
Plotting Precision-Recall Curve
Plotting Residuals Distribution
Plotting ROC Curve
Plotting Observed vs Predictions
Plotting Residuals vs Predictions
Plotting SHAP Plots
Plotting Sobol-Jansen Values Barplot
Plotting Tuner Search Results
Preprocessing Data Matrix
Perform Sensitivity Analysis and Interpretable ML methods
Best Hyperparameters Configuration
Evaluation Results
Integrated Gradients Summarized Results Table
Olden Results Table
Permutation Feature Importance Results Table
SHAP Summarized Results Table
Sobol-Jansen Results Table
A minimalistic library specifically designed to make the estimation of Machine Learning (ML) techniques as easy and accessible as possible, particularly within the framework of the Knowledge Discovery in Databases (KDD) process in data mining. The package provides all the essential tools needed to efficiently structure and execute each stage of a predictive or classification modeling workflow, aligning closely with the fundamental steps of the KDD methodology, from data selection and preparation, through model building and tuning, to the interpretation and evaluation of results using Sensitivity Analysis. The 'MLwrap' workflow is organized into four core steps; preprocessing(), build_model(), fine_tuning(), and sensitivity_analysis(). These steps correspond, respectively, to data preparation and transformation, model construction, hyperparameter optimization, and sensitivity analysis. The user can access comprehensive model evaluation results including fit assessment metrics, plots, predictions, and performance diagnostics for ML models implemented through Neural Networks, Random Forest, XGBoost, and Support Vector Machines algorithms. By streamlining these phases, 'MLwrap' aims to simplify the implementation of ML techniques, allowing analysts and data scientists to focus on extracting actionable insights and meaningful patterns from large datasets, in line with the objectives of the KDD process. Inspired by James et al. (2021) "An Introduction to Statistical Learning: with Applications in R (2nd ed.)" <doi:10.1007/978-1-0716-1418-1> and Molnar (2025) "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (3rd ed.)" <https://christophm.github.io/interpretable-ml-book/>.
Useful links