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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Model Risk Management And the Role of Explainable Models(With Python Code)

A
Ankit Last Updated : 28 Dec, 2020
5 min read

This article was published as a part of the Data Science Blogathon.

Model Risk Management
Photo by h heyerlein on Unsplash

 

Introduction

Similar to rule-based mathematical models, AI/ML models are also validated for inherent risks and the business problem they are intended to solve.

Typically, the model is checked on the following grounds:

* Expected performance

* Is it in alignment with the objective it is meant for?

* Assumptions – are they valid and sound?

* What is the limitation of the serving model?

* The potential impact on the KPI

* Does it adhere to regulatory and ethical mandates? (wherever needed)

 

AI/ML evaluation and validation pointers:

1) Data Integrity:

* Data documentation is critical given that we are dealing with humongous data on daily basis throughout the data pipeline. Data lineage ensures traceability i.e. to maintain data integrity.

* Data pipeline during model training should be synchronous to what is used for production, as multiple model retraining might make it challenging to maintain such dynamic changes.

* Data integrity involves checking the appropriate missing values imputation, data standardization techniques, identifying anomalous patterns, legit labeled data (mislabelled data makes model development a difficult learning process)

2) Feature Engineering:

* Feature Engineering is the heart of the AI/ML model pipeline and makes a significant impact on pattern learning. Having said that, bad feature engineering does equal damage, e.g. data leakage

3) Sampling bias:

* Model should be robust to sampling bias i.e. it should be built on the sample which is an appropriate representation of the population. Imbalanced i.e. under and overrepresentation of any particular class introduces systematic bias to the model

4) Hyper parametrization:

* Change in parameter settings has a direct impact on the model output and needs to be addressed for computational feasibility as well.

5) Explainability:

* AI/ML model ingests a set of input features and outputs the predictions, but how to understand those output predictions is of key interest to gain user’s confidence.

* For simpler models like Linear Regression, we can easily identify the contribution or impact of features on the output variable. But for complex models, such as ensemble models or neural networks, there is no clear path or trajectory which can explain how a certain input vector got transformed into a certain output.

* When model explainability is not transparent, it can entail a lot of other issues which might go unidentified, to the likes of sampling bias, fairness, an appropriate representation of each group, etc.

* Sensitivity and stress testing are necessary to answer the following questions, which embodies the model explainability framework: Key features for each prediction, drivers of actual projections, the difference between ML model and a linear model, working of an ML model, model performance under new states of the world

 

Feature Importance is at two levels – global (training/historical data) and local (each prediction)

Multiple frameworks for model explainability:

* Global: Tree-based, Partial Dependence Plots, Permutation test-based, Global Sensitivity Analysis

* Local: Shapley values, LIME, LRP i.e. layer-wise relevant propagation

Global Explainability using Partial Dependence Plots:

Below is an implementation of PDP using the sklearn library on the Titanic dataset.

feature importance Model Risk Management

The figure shows that Class-1 Passengers had a better chance of survival as compared to Class-3 Passengers

younger passengers had a better chance of survival - Model Risk Management
The figure shows that younger passengers had a better chance of survival 
sex ratio - Model Risk Management

The figure depicts that being male lessens the chance of survival

Model Risk Management
and vice-versa

Local Explainability using Shap values and Lime

Below is the implementation of Shap and lime on the titanic dataset.

Model Risk Management - Shap implementation
SHAP Implementation 
Model Risk Management lime
LIME Implementation

 

I have picked 4 examples from the test set to showcase the sample explainability

 

Example 1:

Outcome: Not survived

outcome
figure of outcome
In this example Embarked_S=0, Age=21 and SibSp=0 have positive contribution in decreasing order whereas Sex_male=1, Sex_female=0,Embarked_Q=1 etc have negative contribution in that order.

 

Feature value contributions(LIME):

feature range
Lime Feature ContributionsExample2:Outcome: Survived
Lime Feature Contributions
Lime Feature ContributionsExample 3:Outcome: Survived
Force Plot (SHAP):- Red and Blue color depicts positive and negative contributions respectively.- Length of the bar depicts the magnitude of contribution.
SHAP Force Plot
Lime Feature ContributionsExample 4:Outcome: Survived
example 4
SHAP Force Plot
contribution
Lime Feature Contributions
In this article, we learned the importance of Model Risk Management, an in-depth understanding of AI/ML specific risk areas, and a deeper understanding of the importance of explainability. We also implemented Global and Local explainability using PDP, SHAP, and LIME frameworks on the Titanic dataset and were able to visualize/compare feature contributions.
Thank you! Keep reading, keep learning!
A
Ankit

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