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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 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

DataHack Radio #20: Building Interpretable Machine Learning Models with Christoph Molnar

Pranav Dar
Pranav Dar Last Updated : 13 Jun, 2019
5 min read

Introduction

How do we build interpretable machine learning models? Or, in other words, how do we build trust in the models we design? This is such a critical question in every machine learning project. But we tend to overlook this in our haste to build more accurate models.

Take a moment to think about this – how many times have you turned to complex techniques like ensemble learning and and neural networks to improve your model’s accuracy while sacrificing interpretability? In a real-world industry/business setting, this is unacceptable.

interpretable machine learning

We need to find a way to use these powerful ML algorithms and still make them work in business setting. So in this episode #20 of our DataHack Radio podcast, we welcome Christoph Molar, author of the popular book – “Interpretable Machine Learning“. Who better to talk about this fundamental and critical topic?

This DataHack Radio episode is full of essential machine learning aspects every data scientist, manager, team lead and senior executive must be aware of. Kunal Jain and Christoph had a multi-layered conversation on several topics, including:

  • Christoph’s interest and foray into interpretable machine learning
  • His research on interpretable ML, specifically model-agnostic methods
  • The ‘Interpretable Machine Learning’ book, and much more!

All our DataHack Radio podcast episodes are available on the below platforms. Subscribe today to stay updated on all the latest machine learning developments!

 

Christoph Molnar’s Background

Statistics is at the core of data science. You cannot simply waltz into the machine learning world without building a solid base in statistics first.

Christoph’s background, especially his university education, embodies that thought. He has a rich background in statistics. Both his Bachelor’s and Master’s degrees were in the area of statistics from the Ludwig-Maximilians Universität München in Germany.

During this stretch, Christoph came across machine learning and was instantly fascinated with it. He started taking part in online ML competitions and hackathons. It didn’t take him long to figure out that linear regression, useful as it was in terms of learning, wasn’t going to cut in these hackathons.

So, he delved deeper into the domain. Decision trees, random forest, ensemble learning – Christoph didn’t leave any stone unturned in his quest to learn and master these algorithms. Once he finished his Master’s, he worked as a Statistical Consultant for a couple of years in the medical domain before stints at a few other organizations.

During this period, Christoph was also doing his own research on the side. Any thoughts on what his area of interest was? You guessed it – interpretable machine learning.

 

Interest in Interpretable Machine Learning

interpretable machine learning

Source: xkcd

Interpretable machine learning is not a topic we come across often when we’re learning this domain (or even working on it). Everyone knows about it yet only a few truly discuss it. So what triggered Christoph’s interest in this area of research?

For Christoph, it all hearkens back to his university days. This was taught as part of his statistics education. So when he was learning about a certain topic, like linear or logistic regression, he learned it from the ground up. That involved not just learning how to built a model, but also how to interpret the inner workings that generated the final output.

A big reason for delving into interpretable ML was Christoph’s experience with non-machine learning folks (and I’m sure everyone would have experienced this at some point):

“I used to ask people “Why don’t you use machine learning for the problem you’re working on?” The answer was always “We can’t explain how it works. Management will not accept a black box model.””

If that sounds familiar, you’re not alone! This inability to understand how models work is quite prevalent in the industry. No wonder a lot of “machine learning projects” fail before they’ve had a chance of picking up steam.

All of this learning naturally translated into Christoph’s machine learning forays. He started exploring methods to make machine learning models interpretable, including looking at projects, reading research papers, etc. One of the methods he came across is called LIME, or Locally Interpretable Model-Agnostic Explanations. We have an excellent article around this you should check out:

According to Christoph, there wasn’t one specific blog or tutorial which emphasized interpretable machine learning across techniques. And that was how the idea of writing a book on the topic was born.

 

Machine Learning Interpretability Research

Christoph’s research into machine learning interpretability is focused on model-agnostic methods (as opposed to model-specific methods). The former approach is more generalizable in nature while the latter deep dives into the model at hand.

machine learning interpretability

For the model-agnostic methods, they work by changing the features of the input data and observing how the predictions change. For example, how much does the performance of a model drop if we remove a feature? This helps understand feature importance as well. You’ll have come across this concept while learning the random forest technique.

You might be inclined to think – wouldn’t model-specific methods be better? It’s a fair question. The advantage of model-agnostic methods is that they adapt to the evolving spectre of machine learning models. This is applicable to complex techniques like neural networks, and even those that haven’t become mainstream yet.

If you’re a R user, make sure you check out Christoph’s interpretable machine learning package called ‘iml’. You can find it here.

Here, Christoph mentioned a very valid point about the definition of interpretability. Everyone seems to have a different understanding of the concept. A business user might be happy with an overview of how the model works, another user might want to fully grasp each step the model took to produce the final result. That fuzziness is a challenge for any researcher.

 

End Notes

I strongly believe this topic should be covered in every machine learning course or training. We simply cannot walk into an industry setting and start building a complex web of models without being able to explain how they work.

Can you imagine a self-driving car malfunctioning and the developers struggling to understand where their code went wrong? Or a model detecting an illness where none exists?

I hope to see more traction on this in the coming days and weeks. Until then, make sure you listen to this episode and share it with your network. I look forward to hearing your thoughts and feedback in the comments section below.

data science podcastDataHack Radiomachine learning
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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