Using Markdown for Machine Learning Model Documentation¶

Using Markdown files for Model Documentation

This tutorial covers the process of documenting machine learning models using Markdown. It demonstrates how to create a comprehensive and readable documentation file, covering aspects like model training, parameters, and metadata.

Overview¶

Markdown provides a simple yet effective way to document machine learning models and their metadata.
This tutorial uses the example of a linear regression model trained on the Boston housing dataset.

Creating Your Markdown Documentation¶

Step 1: Document Overview¶

Start with an overview of what your model does and the dataset used.

# Linear Regression Model Documentation

## Overview
This document describes the process of training a Linear Regression model using the Boston housing dataset. It details the generation of a metadata file capturing essential information about the model parameters, performance metrics, and data characteristics.

Step 2: Model Training Description¶

Describe how the model is trained, including dataset details and model parameters.

## Model Training
The model is trained using the Boston housing dataset from Scikit-Learn. We perform a basic train-test split, train a Linear Regression model using the training data, and then evaluate its performance on the test data.

### Data
- **Dataset Used**: Boston Housing Dataset
- **Features**: CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD, TAX, PTRATIO, B, LSTAT
- **Target**: Housing Price

Step 3: Detailing Model Parameters¶

List the parameters used in the model training.

## Model Parameters
The model is trained with the following parameters:
- **fit_intercept**: true
- **normalize**: false
- **copy_X**: true
- **n_jobs**: null

These parameters are the default settings for Scikit-Learn's LinearRegression model.

Step 4: Performance Metrics¶

Explain the performance metrics used to evaluate the model.

## Performance Metrics
The model's performance is evaluated using the following metrics:
- **Mean Squared Error (MSE)**
- **R-squared (R2)**

The values for these metrics are computed based on the model's predictions on the test set.

Step 5: Metadata File Description¶

Describe the metadata file generated along with the model.

## Metadata File
Alongside the model, a metadata file (`service_sage_v1.2.0_linearReg_20240123_metadata.json`) is generated. This JSON file includes:
- **Model Name**: Linear Regression
- **Timestamp**: Date when the model is trained and metadata is generated.
- **Model Parameters**: A list of parameters used to train the model.
- **Performance Metrics**: MSE and R2 values calculated from the test set.
- **Data Description**: Brief description of the dataset used.
- **Feature Names**: List of feature names from the dataset.
- **Target Name**: Name of the target variable.

Step 6: File Generation Process¶

Conclude with details about how the model and metadata files are generated.

## File Generation
The model is saved as a pickle file (`service_sage_v1.2.0_linearReg_20240123.pkl`), and the metadata is stored in a JSON file (`service_sage_v1.2.0_linearReg_20240123_metadata.json`). These files provide a snapshot of the model at the time of training, along with relevant information about its performance and configuration.

Conclusion¶

This Markdown file offers a concise yet comprehensive overview of the model training and metadata generation process, suitable for inclusion in project documentation or a repository readme.