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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

Questions to ask while designing A/B (or multi-variate) tests

Kunal Jain
Kunal Jain Last Updated : 04 Nov, 2013
4 min read

Testing / experimentation can help Organization find hidden information or insights about their own customers and business.

Sadly, not many Organizations realize the amount and value of learning which can happen through testing. On one hand, there are Organizations like Google, Amazon, Capital One where testing is an integral part of the culture. On the other hand, there are Organizations which are unaware of testing and how to use it for generating key insights about your business.

 

[stextbox id=”section”]For the starters, what is testing / experimentation?[/stextbox]

Simply put, testing is learning through experimentation. Let us take a simple example. Imagine that you are responsible for Sales conversion of new customers for an e-commerce retailer. Your website currently has a 4 step check-out process. Your hypothesis is that by condensing this form to 3 steps (there by increasing length of some steps), you will be able to increase the conversion.

While you can try and answer this basis data (by looking at competitors, past experience (if any)), you can not find out the answer until you test it out. So, the easiest solution is to take half of your customers through proposed 3 step process (commonly called Champion) and the other half through existing 4 step process (Challenger or Control group). Run the test long enough to have statistically significant reads on conversion and check out what works for  your customers and your website.

For reading more on need and importance of testing, click here

Schematic of AB testing

Schematic of AB testing

[stextbox id=”section”]The process of testing:[/stextbox]

While the process of testing is simple, it requires high level of diligence and attention to details. To some extent, the skill of testing and analyzing tests effectively is like a vintage wine. The more time you spend practicing, the better you become at it.

Following is a schematic of how life cycle of a typical test looks. Overall process can be divided in 4 parts:

  1. Define
  2. Design
  3. Implement
  4. Analyze

In order to make sure that you get the maximum value out of any testing, it is very critical that you get the design right.

Process of testing

Process of testing

While each of these step is critical, the focus of this post is to provide a list of questions to be asked at design stage in order to gain the most out of these tests. These questions can suffice as a checklist to make sure you have considered all critical aspects of test design.

[stextbox id=”section”]Questions to ask when designing test:[/stextbox]

  1. What is (are) the key hypothesis you want to test? Which metric would you measure to test this hypothesis?
  2. How many test cells do you need to test out your hypothesis? Each version of treatment should be considered as a test cell.
  3. What is the sample size requirement for each test cell? How many data points do you need to make the reads statistically significant?
  4. How long do you need to run the test? Ideally this should be the period in which you can read the difference + some buffer.
  5. What controls do you have in place to make sure that testing happens as intended?
  6. What is the monitoring plan for the test? How frequently would the test be monitored? Who will perform the monitoring? What would be the escalation matrix if monitoring does not happen as planned?
  7. What are the key risks to the success of this test? How can you mitigate these risks?
  8. Was there any test performed in past to test the same / similar hypothesis? What was the outcome of that test?
  9. How would the assignment of customers / events to a test cell happen? Would it be random?
  10. If the assignment to test cell is not random, how will you make sure that there is no bias due to method of assigning test cell?
  11. If it is random, how is the random number generated? Does it require a seed? How can you make sure that you do not provide same seed every time?
  12. Is there a need to provide consistent treatment every time the event happens? How are you ensuring that? If there a CRM solution which you are using for this?
  13. Are there any co-variates you need to measure during the testing? What is the impact of measuring the co-variates on sample size requirement?
  14. Are your stakeholders aware of the testing procedure and requirement? How do you plan to make sure there are no strategic changes which happen while the test is running (which can impact the reads)?
  15. Are there any confounding errors which could result in mis-interpretation of results?
  16. How long will it take for results of test to come out? Is there any gestation period required?
  17. What will you do if something goes wrong during testing? Will you stop the test and re-run? or you would continue with the test?
  18. What is the maximum duration for which you can run the test? What level of lift and confidence interval with which you can read these results?
  19. Last, but not the least, what is the expected benefit from the test?

Answering these 19 questions in a disciplined manner will help you identify any gaps in your test design. More often than not, that would mean significant savings in terms of resource and opportunity costs.

What do you think about these questions? Do you think they can help you create a better test design? Are there any other question you think should be part of this list? Please share your thoughts on these questions in the comments below.

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Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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