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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
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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
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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
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8 Ways to Improve Accuracy of Machine Learning Models
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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
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Interview Questions on AdaBoost Algorithm in Data Science

Parth Shukla
Parth Shukla Last Updated : 21 Nov, 2022
5 min read

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

Introduction

AdaBoost is a boosting algorithm used in data science. It is one of the best-performing and widely used algorithms. In data science interviews, there are lots of questions asked related to the AdaBoost algorithm, whether a working mechanism, the mathematics behind it, or the graphical intuition. In this article, we will cover some of the most asked questions related to the AdaBoost algorithm in data science Interviews.

Read more articles on the AdaBoost algorithm.

AdaBoost Algorithm
Source: Resource.biginterview.com

Let’s start solving the questions by going through them one by one.

What are Weak Learners and Decision Stumps in Machine Learning?

Weak Learners in Machine Learning are ML algorithms with very low accuracy, almost just above 50% almost all the time. It means it performs poorly in the test dataset and gives unreliable results.

Decision Stumps are a type of decision tree having a maximum depth of one. Here we can clearly understand that decision trees having maximum depth will have very low accuracy; hence it is also the type of weak learner.

Hence the decision stumps are weak learners. They will have only one split geometrically in the dataset, and the split will be based on accuracy.

However, combining multiple weak learners gives higher and more reliable accuracies, so in AdaBoost, multiple weak learners are trained. In the end, we get a strong learner having higher accuracies. In AdaBoost, any algorithm can be selected for being a weak learner. However, in AdaBoost, decision stumps are often chosen for a weak learner.

Choosing decision trees as a weak learner is for getting higher accuracies and to see what the model is doing in the backend while training and when trained.

Why is AdaBoost known as Stagewise Additive Method?

As AdaBoost is a boosting algorithm, it adds the results of multiple weak learners to get the final strong learner. Hence it is known as the additive method.

Here the term stagewise means that in AdaBoost, one weak learner is trained, now whatever the errors that are present in the first stage while training, the first weak learner will pass to the second weak learner while training to avoid the same error in the future stages of training weak learners. Hence it is a Stagewise method.

Here we can see the stagewise addition of weak learners scenario in SdaBoost hence it is known as the Stagewise Addition method.

How does AdaBoost Works? Explain the Core Intuition of the Algorithm with Decision Stumps as Weak Learners.

If decision stumps are the weak learners for the AdaBoost algorithm, then there will be decision trees with max depth one and learning algorithms.

In the first stage, the first weak learner will be taken, and they will split the dataset into two parts by splitting. Here the splitting will be done based on accuracy. Once the first weak learners have done the split, it will have many errors as it was only a decision tree with max depth one.

Now, whatever errors are in the first stage will be passed to the 2nd stage for training the second weak learner. While training the second weak learner, the errors made during the first stage will be taken care of.

So in every stage, the error term will be calculated, which will reduce as we move to the further stages by avoiding the mistakes made by the previous weak learners.

What is the Alpha Term in AdaBoost? How does it work?

In AdaBoost, multiple weak learners are trained to get the strong learner. As a result, hence calculating the error term of every weak learner is essential to know which weak learner is performing best and which is not.

The term Alpha is a parameter that indicates the weight that should be given to a particular weak learner algorithm. If the value of the term Alpha for a particular algorithm is high, that indicates that the model is performing best and the error rate for the same is low.

The formula to calculate the Alpha term is related to the error term, which is as follows.

AdaBoost Algorithm

Here in the above image, we can see that the Alpha term is completely dependent on the error rate of the particular weak learner, which means that the more the error less the alpha term value and hence less the weight to the algorithm.

How are Weights Updated in AdaBoost?

In AdaBoost, whatever mistakes the previous weak learners make errors are passed to the next weak learners to avoid the same mistake. Now, to inform the next weak learner about the previous weak learner’s mistake, we will increase the weightage of the samples on which the mistakes (misclassification) are made by performing the upsampling.

Now here, the aim is to increase the weight of misclassified points to update the weight properly.

For Correctly classified points, in which the algorithm makes no mistake, the weight updation formula will be as follows.

AdaBoost AlgorithmFor Incorrectly classified points in which the algorithm makes a mistake, the weight updation formula would be:

Conclusion

In this article, the interview questions related to AdaBoost algorithms are discussed with the core intuition, the mathematics, and the core formulas with good reasoning of all questions. Practising these questions would not only help one to develop an idea about the algorithm but will also help one to answer the questions in a better way with mathematical formulations and theoretical knowledge.

Some key takeaways from this article are:

1. AdaBoost is a boosting algorithm known as the stagewise additive method, as it follows weak learners’ addition in subsequent training stages.

2. Most of the time, decision stumps are used as weak learners in AdaBoost for getting an idea about the backend of the algorithm and higher accuracies.

3. In AdaBoost, the error rate is less than the Alpha term, hence less weightage to the weak learner. So in the weight updation, weak learners who make mistakes will be given higher weightage through the Alpha term.

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

AdaBoostalgorithmsblogathon
Parth Shukla
Parth Shukla

UG (PE) @PDEU | 50+ Published Articles on Data Science | Technical Writer (AI/ML/DL) | Data Science Freelancer | Amazon ML Summer School '22 | Reach Out @[email protected], @portfolio.parthshukla.live

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Guide on AdaBoost Algorithm
Guide on AdaBoost Algorithm
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Top 10 Interview Questions on Gradient Boosting Algorithms

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