Building a Classification Model on Pima Diabetes Dataset with SHAP and Explainer Dashboard

The Pima Indians Diabetes Database is widely used for developing various machine learning models and is especially popular in the field of medical predictive modeling. This dataset originates from the National Institute of Diabetes and Digestive and Kidney Diseases and aims to predict whether or not a patient has diabetes, based on certain diagnostic measurements.

Photo by Mario Heller on Unsplash

The dataset comprises several medical predictor variables including:

• Number of pregnancies

• Plasma glucose concentration

• Diastolic blood pressure

• Triceps skinfold thickness

• 2-Hour serum insulin

• Body mass index (BMI)

• Diabetes pedigree function

• Age

And one target variable:

• Outcome (0 or 1, indicating non-diabetic or diabetic, respectively)

The dataset is significant for several reasons. It helps researchers and data scientists to build models that can predict whether a patient is likely to develop diabetes based on various health metrics. This is crucial for early intervention and management of the disease.

Classification Model

Classification models in machine learning are a type of supervised learning models which predict the categorical class labels of new instances, based on past observations. They are used when the output variable is a category, such as ‘yes’ or ‘no’, ‘spam’ or ‘not spam’. They are implemented using algorithms like logistic regression, decision trees, random forest, gradient boosting, and support vector machines among others. For the Pima Diabetes dataset, a classification model would predict whether a person has diabetes or not based on various input features.

SHAP (SHapley Additive exPlanations)

SHAP (SHapley Additive exPlanations) is a unified measure of feature importance that assigns each feature an importance value for a particular prediction. SHAP values interpret the output of machine learning models using a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. SHAP values provide a measure for the average marginal contribution of a feature value to all possible coalitions of features.

Explainer Dashboard

Explainer Dashboard is a Python library that allows you to build an interactive web-based dashboard for explaining the predictions and workings of your machine learning model with just a few lines of code. It leverages the SHAP library to provide insights into the model’s decision-making process and enables users to analyze the impact of different variables on model predictions. Explainer Dashboard is especially useful for data scientists and analysts who need to communicate their model results and interpretations to non-technical stakeholders, as it provides a user-friendly interface that makes it easy to interpret machine learning models.

In summary, utilizing the Pima Diabetes dataset, one can build a classification model to predict diabetes occurrences. Further, SHAP and Explainer Dashboard can be employed to interpret, understand, and communicate the model’s predictions and decision-making mechanisms in a coherent and transparent manner.

Description of the Pima Diabetes Dataset