MLwrap

What the Package Does (One Line, Title Case)

https://github.com/jmartinezgarcia/mlwrap

Repository

What the Package Does (One Line, Title Case)

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# MLwrap

A minimalistic library specifically designed to make the estimation of MachineLearning (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, Support Vector Machines, Random Forest, and XGBoost
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.

## Installation

You can install the development version of MLwrap from [GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("JMartinezGarcia/MLwrap")
```

## Example

This is a basic example which shows you how to solve a common problem:

```{r example}
library(MLwrap)
## basic example code

formula_reg <- "psych_well ~ age + gender + socioec_status + emot_intel + depression"

analysis_object <- preprocessing(sim_data, formula_reg, task = "regression") |>

                   build_model(model_name = "Random Forest",
                                       hyperparameters = list(trees = 150)) |>

                   fine_tuning(tuner = "Bayesian Optimization", metrics = "rmse") |>
   
                   sensitivity_analysis(methods = c("PFI", "SHAP"), 
                                        metric = "rsq")

### Tuning Results

analysis_object |>
  plot_tuning_results()

### Evaluation Plots

analysis_object |>
  plot_residuals_distribution() |>
  plot_scatter_residuals() 

### Sensitivity analysis

analysis_object |>
  plot_pfi() |>
  plot_shap()

table_pfi <- table_pfi_results(analysis_object)

show(table_pfi)

```

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