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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

Common A/B Testing Questions Asked During Interviews

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Guest Blog Last Updated : 31 Mar, 2022
4 min read

A/B Testing Interview Questions

Source: Unsplash

Introduction

Applying for jobs and preparing for multiple rounds of interviews with multiple companies can be more stressful than the existing job for many. The anticipation of what might be asked and how it might be asked can give sleepless nights. Today, I am going to try covering a tiny topic from the perspective of interview related questions for a data scientist role. I have already covered some key A/B testing concepts in previous posts. Let’s try to peek into what kind of questions might be asked to test whether you have theoretical knowledge or practical knowledge as well.

A/B Testing Interview Questions

  1. When should we do an A/B test?

An A/B test is usually run to test the success of any change in an existing feature or to test the impact of a new feature. Taking an example from Udacity’s A/B testing course, an A/B test can help you climb the peak of your current mountain. But it cannot help you decide which mountain you should climb.

  1. What is the first step in running an A/B test?

Once a product manager comes to you with an idea to test, do not just jump to setting up a campaign. Before even setting up and running an A/B test, there are some key steps that need to be done

  1. Define the null and alternative hypothesis
  2. Define your north star metric and guardrail metrics
  3. Power analysis — to determine either sample size or minimum detectable effect for your north star metric
  4. Create a test plan
  5. Work with engineers/instrumentation teams to get appropriate tags in place
  6. Make sure the tags are working 
  7. Get a sign off on the test plan from product managers and get the tags once again validated by engineers
  8. What is a null and alternative hypothesis?

The null hypothesis states that there is no difference between test and control. The alternate hypothesis states that there is a difference between test and control

  1. What is the difference between one-tailed vs. two-tailed tests?

One-tailed tests check for the possibility of change in only one direction while two-tailed tests check for the possibility of change in both positive and negative directions

  1. How would you explain the p-value to a layman?

For a particular test run, the p-value tells us that assuming the null hypothesis is true, meaning there is no difference between test and control, what are the chances of getting the results that we have by chance

  1. What are alpha and beta?

Alpha, also called the significance level, tells us the probability of type I error. Beta gives us the probability of type II error i.e. failing to reject the null hypothesis when it’s false

  1. What are type I and type II errors?

Type I error means rejecting a null hypothesis when it’s true i.e. there wasn’t any difference between test and control but we conclude that there is a difference. Type II error means failing to reject the null hypothesis when it’s false i.e. there was a difference but we couldn’t pick it up

  1. How long should you run a test?

Based on estimated daily visitors and the number of variations, you can calculate the test duration. For example, if your website gets daily traffic of 10k and the required sample size is 100k and the numbers of variants are 2 i.e. test and control, then the test should be run for 20 days – (100k/10k)*2

It is also advised to run an A/B test for at least 2 weeks to control for any variations due to weekdays vs weekends.

  1. How to conclude the results of your test?

It depends on a few things — 

  1. The north star metric should be significantly positive (or neutral depending on what you are testing)
  2. The P-value should be less than the alpha value
  3. The upper and lower confidence interval should have minimal difference 
  4. The lift% should not be much lesser than the minimum detectable effect that we care for our primary metric
  5. The daily trend of lift% of metrics should have the same sign on most days — sign test
  6. Finally, the guardrail metrics should be neutral, if not positive
  1. Can your treated samples be different from the assigned samples?

Yes. An assigned sample means anyone who became a part of that campaign (test or control). A treated sample is a subset of the assigned sample, based on additional conditions. For example, if you are running an A/B test on Search pages of your website, you may want to measure results only for visitors who saw Search pages within an assigned campaign

Conclusion

Knowing the theory is usually not enough. The more experiments you run, the better you’ll get at it. Every experiment teaches something new. This is more like a cheat sheet to revise the concepts. Hope this list of A/B testing interview questions helps in reducing some levels of anxiety! 

Read the latest articles on our blog!

References:

  1. Udacity’s Course on AB Testing
  2. https://www.statisticshowto.com/
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