Machine Learning Modelling for Everyone
Create ML Model
Fine Tune ML Model
Pipe operator
Plot Accumulated Local Effects (ALE)
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
Plot Partial Dependence (PDP)
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
Friedman's H-Statistic Table
Integrated Gradients Summarized Results Table
Olden Results Table
Friedman's H-Statistic Pairwise Interaction Table
Permutation Feature Importance Results Table
SHAP Summarized Results Table
Sobol-Jansen Results Table
MLwrap Comprehensive Tutorial
A minimal 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 essential tools to 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(). It also includes global and pairwise interaction analysis based on Friedman’s H-statistic to support a more detailed interpretation of complex feature relationships.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' (Extreme Gradient Boosting), and 'Support Vector Machines' (SVM) 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.