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Reading list

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 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

Explainable AI: What make it Crucial for Model Interpretation?

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Guest Blog Last Updated : 15 Jul, 2024
7 min read

Introduction

Artificial intelligence (AI) helps us solve real-world problems by processing data. We turn real-world information into numbers for AI models to understand and improve. But a big question remains: How do we make sense of AI results in the real world? This is where Explainable AI comes in. It’s all about making AI’s decisions easy to understand and apply in real-life situations. In this article, we’ll explore Explainable AI, its importance, and how it helps bridge the gap between AI models and practical use.

Explainable AI

Table of contents

What is Explainable AI or XAI?

Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology such that the results of the solution can be understood by humans. In the early phases of AI adoption, it was okay to not understand what the model predicts in a certain way, as long as it gives the correct outputs. Explaining how they work was not the first priority. Now, the focus is turning to build human interpretable models.

Why Does Explainable AI Matter?

Explainable AI (XAI) matters because it helps us understand how AI systems work and make decisions. This is important for several reasons:

  • Trust: When we trust an AI system, we are more likely to use it and make decisions based on its output. XAI helps us build trust in AI systems by explaining their decisions.
  • Transparency: XAI helps us understand AI systems’ potential biases and limitations. They can significantly impact our lives, and we need to know the potential risks and benefits before using them.
  • Accountability: It can help us hold AI systems accountable for their decisions. They are often used in high-stakes applications like law enforcement and healthcare.

How to Interpret Model with XAI?

Model interpretability can be examined in two levels:

  • Global Interpretation: Examines the model from a broader perspective. For example, let’s say we are working on a house price dataset and we implemented a neural network. The global interpretation might say “Your model uses # of squared feet as an important feature to derive predictions
  • Local Interpretation: As the name suggests, this approach is focused on a certain observation/data point. Let’s continue moving forward with our example. Prediction for a really small house turned out large. Local interpretation looks at the other features and it might say “Your model predicted this way because the location of the house is very close to the city center.
Interpretation scope explainable AI
          
PDP

How explainable AI works?

There are three main components that hoq explainable ai works:

Creating an easily understandable model involves selecting or designing models that are inherently more simple for humans to comprehend. An example of this is when a decision tree articulates its logic through a sequence of branching queries.

Describing post-analysis: This includes utilizing different techniques to evaluate a current model and generate interpretations for its results. Methods such as feature importance identify the data inputs that had the biggest impact on the decision.

Concentrating on the utilization of data: This may include examining biases in the training data or confirming the data’s relevance to the current task. Ultimately, the concept of “garbage in, garbage out” is also relevant in the context of AI.

The Trade-off Between Accuracy and Interpretability

In the industry, you will often hear that business stakeholders tend to prefer models that are more interpretable like linear models (linear\logistic regression) and trees which are intuitive, easy to validate, and explain to a non-expert in data science.

In contrast, when we look at the complex structure of real-life data, in the model building & selection phase, the interest is mostly shifted towards more advanced models. That way, we are more likely to obtain improved predictions.

Challenge of Complex Models

Models like these (ensembles, neural networks, etc.) are called black-box models. As the model gets more advanced, it becomes harder to explain how it works. Inputs magically go into a box and voila! We get amazing results.

But, HOW?

When we suggest this model to stakeholders, will they completely trust it and immediately start using it? NO. They will ask questions and we should be ready to answer them.

  • Why should I trust your model?
  • Why did the model take a certain decision?
  • What drives model predictions?

We should consider both improving our model accuracy and not get lost in the explanation. There should be a balance between both.

Resource: DPhi Advanced ML Bootcamp — Explainable AI [2]

Here, I would like to share a sentence from Dipanjan Sarkar’s medium post about explainable AI:

Any machine learning model at its heart has a response function which tries to map and explain relationships and patterns between the independent (input) variables and the dependent (target or response) variable(s).

Role of Data in Model Trustworthiness

So, models take inputs and process them to get outputs. What if our data is biased? It will also make our model biased and therefore untrustworthy. It is important to understand & be able to explain to our models so that we can also trust their predictions and maybe even detect issues and fix them before presenting them to others.

Techniques for Model Interpretability

To improve the interpretability of our models, there are various techniques some of which we already know and implement. Traditional techniques are exploratory data analysis, visualizations, and model evaluation metrics. With the help of them, we can get an idea of the model’s strategy. However, they have some limitations. To learn more about traditional ways and their limitations.

Modern Techniques for Model Interpretability

Other model interpretation techniques and libraries have been developed to overcome limitations. Some of these are :

  • LIME ( Local Interpretable Model-Agnostic Explanations)
  • SHAP (Shapley Additive Explanations)
  • ELI5 (Explain Like I’m 5)
  • SKATER

These libraries use feature importance, partial dependence plots, individual conditional expectation plots to explain less complex models such as linear regression, logistic regression, decision trees, etc.

Feature importance shows how a feature is important for the model. In other words, when we delete the feature from the model, how our error changes? If the error increases a lot, this means that a feature is important for our model to predict the target variable.

feature importance

Partial dependence plots visualize the effect of the change for a certain feature when everything else is held constant (with a cooler phrase: ceteris paribus). With the help of these, we can see a possible limit value, where this value is exceeded, it directs the model predictions the other way. When we are visualizing partial dependence plots, we are examining the model globally.

Model-Agnostic Techniques

Individual conditional expectation plots show the effect of changes for a certain feature, just like partial dependency plots. But this time, the point of view is local. We are interested to see the effect of changes for a certain feature for all instances in our data. A partial dependence plot is the average of the lines of an ICE plot.

individual conditional expectation plots

When it comes to explaining more advanced models, model-agnostic (does not depend on the model) techniques are used.

Global surrogate models take the original inputs and your black-box machine learning predictions. When this new dataset is used to train and test the appropriate global surrogate model (more interpretable models such as linear model, decision tree, etc.), it basically tries to mimic your black-box model’s predictions. By interpreting and visualizing this “easier” model, we get a better understanding of how our actual model predicts in a certain way.

Other interpretability tools are LIME, SHAP, ELI5, and SKATER libraries. We will talk about them in the next post, over a guided implementation.

Conclusion

The imperative to demystify these complex models is undeniable in a world increasingly shaped by AI. The power of Explainable AI lies in its ability to bridge the gap between inscrutable “black-box” algorithms and human understanding.

 As this article has elucidated, integrating Explainable AI techniques equips us with the means to provide convincing explanations for AI model decisions. By doing so, we foster trust, ensure accountability, and enable better decision-making. As we journey into the age of AI, embracing transparency through Explainable AI is not just a choice; it’s a necessity. It empowers us to harness the full potential of AI, making its inner workings accessible to all. Also, in this article we have talk about the Explainable ai Examples.

Frequently Asked Questions

Q1. What is an example of explainable AI?

A. An example of explainable AI is a decision tree model used for credit scoring. It provides a clear, step-by-step explanation of how it assigns credit scores based on income and credit history.

Q2. What is the difference between explainable AI and AI?

A. The difference between explainable AI and AI is that explainable AI focuses on making the decision-making process of AI models transparent and interpretable for humans, while AI, in general, encompasses a broader range of machine learning and problem-solving techniques.

Q3. What is explainable AI for question answering?

A. Explainable AI for question answering involves AI models that can provide both answers and transparent explanations for how they arrived at those answers, helping users understand the reasoning behind the responses.

Q4. What is the methodology of explainable AI?

A. The methodology of explainable AI often involves techniques such as feature importance analysis, partial dependence plots, and model-agnostic methods like LIME and SHAP. These methods aim to elucidate how AI models make decisions while maintaining transparency and interpretability.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

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