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What is explainX.ai?

Getting Started

Installation

Explainability Dashboard

Explainability Modules

Starter Example

Tutorials

Predicting Risk with CatBoost

Analyzing Dashboard

Starter Example

This is a basic example of explainX Open-Source usage in explaining a Random Forest model.

After successfully installing explainX, open up your Python IDE of Jupyter Notebook and simply follow the code below to use it:
 Open up your jupyter notebook and simply follow the code. 
Goal
The goal is to be able to explain and debug the machine learning model we are building. The main goal will be to provide business-level explanations. 
Prerequisite
Make sure you have Sklearn & explainX installed. In case you don't, follow the following command to install the libraries. 
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Import required module.
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from explainx import * 
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import train_test_split
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1.
Load and split your dataset into x_data and y_data
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#Load Dataset: X_Data, Y_Data 
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#X_Data = Pandas DataFrame
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#Y_Data = Numpy Array or List
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X_data,Y_data = explainx.dataset_heloc()
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Split dataset into training & testing.
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X_train, X_test, Y_train, Y_test = train_test_split(X_data,Y_data, test_size=0.3, random_state=0)
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Train your model.
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# Train a RandomForest Model
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model = RandomForestClassifier()
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model.fit(X_train, Y_train)
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After you're done training the model, you can either access the complete explainability dashboard or access individual techniques.
Complete Explainability Dashboard
To access the entire dashboard with all the explainability techniques under one roof, follow the code down below. It is great for sharing your work with your peers and managers in an interactive and easy to understand way.
5.1. Pass your model and dataset into the explainX function:
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explainx.ai(X_test, Y_test, model, model_name="randomforest")
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5.2. Click on the dashboard link to start exploring model behavior:
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App running on https://0.0.0.0:8080
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Explainability Modules
In this latest release, we have also given the option to use explainability techniques individually. This will allow the user to choose technique that fits their personal AI use case.
6.1. Pass your model, X_Data and Y_Data into the explainx_modules function.
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explainx_modules.ai(X_test, Y_test, model)
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As an upgrade, we have eliminated the need to pass in the model name as explainX is smart enough to identify the model type and problem type i.e. classification or regression, by itself.
You can access multiple modules:
Module 1: Dataframe with Predictions
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explainx_modules.dataframe_graphing()
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Module 2: Model Metrics
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explainx_modules.metrics()
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Module 3: Global Level SHAP Values
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explainx_modules.shap_df()
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Module 4: What-If Scenario Analysis (Local Level Explanations)
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explainx_modules.what_if_analysis()
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Module 5: Partial Dependence Plot & Summary Plot
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explainx_modules.feature_interactions()
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Module 6: Model Performance Comparison (Cohort Analysis)
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explainx_modules.cohort_analysis()
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To access the modules within your jupyter notebook as IFrames, just pass the mode='inline' argument in each of the function.
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What-If Scenario Analysis

Next - Tutorials

Predicting Risk with CatBoost

Last modified 10mo ago

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Contents

Goal

Complete Explainability Dashboard

Explainability Modules