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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

Interview Questions on Bagging Algorithms in Machine Learning

Parth Shukla
Parth Shukla Last Updated : 24 Jul, 2024
5 min read

Introduction

Bagging is a famous ensemble technique in the field of machine learning which is widely used for its performance and better results. It is one of the most important and high-performing ensemble techniques, which is easy to use and accurate. Due to the rich performance on even weak machine learning algorithms, It has become a popular ensemble technique and is being compared with other strong machine learning algorithms.

Most machine learning interviews frequently asked interview questions related to bagging algorithms. This article will discuss the top interview questions on bagging, which are mostly asked in machine-learning interviews. Practicing these questions will help one understand the concept of bagging very deeply and help answer the interview questions related to it very efficiently.

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

Bagging interview questions
 Source: https://resources.biginterview.com/wp-content/uploads/2022/07/Panel-Interview-101-1080×675.jpg

1. What is Bagging and How Does it Work? Explain it with Examples.

Bagging stands for Bootstarp Aggregation. Bootstrapping generally means randomly selecting a sample from a dataset, and aggregations stand for the further procedure and preprocessing of the selected samples. So in the bagging, we generally take multiple machine learning models of the same algorithm, meaning that we only take the same machine learning algorithm multiple times.

For example, if we are using SVM as a base algorithm and the count of the models is 5 then all the models will be of SVM only. Once the base model is decided, there will be a bootstrapping process where the random samples from the dataset will be selected and fed to the machine learning model.

The data will be fed to the models by bootstrapping, and there will be separate training for every model. Once all the models are trained, then there will be a prediction phase where all the different models will predict individually, and as a step of aggregation, we can apply any method to the multiple prediction data as there will be 5 different predictions from every model. The common approach is to calculate the mean of the predictions in case of regression or consider the majority count of it in case of classification.

2. How is Bagging Different from the Random Forest Algorithm?

The very basic difference between bagging and the random forest is related to the base models. In bagging, the base model can be any machine learning algorithm, and there is an option of selecting any machine learning algorithm as the base model in bagging by using the base_estimator parameter.

In the random forest, the base estimator or the base models are always decision trees, and other is not any option of selecting any other machine learning algorithms as base estimators in random forest.

Another difference between bagging and the random forest is that in bagging, all the features are selected for the training of the base models, whereas in the random forest, only a subset of the features are selected for the base model training, and out of that only the best performing are chosen as final features.

3. What is the Difference Between Bootstrapping and Pasting in Bagging?

The main difference between bootstrapping and pasting is in the data sampling. As we know, in bagging, there is a sampling of the main dataset, It could be row or column sampling, out of which samples of the dataset are provided to the base models for training.

In bagging or bootstrapping, the samples are taken from the main dataset and fed to the first model, now the same samples can be again used for the training of any other method;, here, the sampling will be with replacement.

In pasting, there is a sample taken from the main dataset, but once the samples are used for training any model, the sawm samples will not be used again for the training of any other model. So here, the sampling is done without replacement.

4. Why does Bagging Performs Well on the Low Bias High Variance Datasets?

In general, low bias high variance datasets are the data that have a very good performance on the training data and poor performance on the testing data, the case of overfitting. The data prone to overfit on any model is preferred for bagging algorithms as bagging reduces the variance of the dataset. Now let’s suppose we have a dataset which is having a very high variance. Suppose we have 10000 rows in our data from which 100 samples have a high variance; now, if this data is fed to any other algorithm, the algorithm will perform poorly as these 100 samples will affect the training, but in the case of bagging, there will be multiple models of the same algorithm, so there will not be a case where all the 100 rows will be fed to the same model due to bootstrapping or sampling of the data.So here now every model will experience the same weightage of the variance in the dataset, and in the end, the high variance of the dataset will not affect the final predictions of the model.

5. What is the Difference between Bagging and Boosting? Which is Better?

In the bagging algorithms, the main dataset is sampled in the parts, and the same multiple base models are used for training with different samples. In the final stage of aggregation, the output from every single base model will be considered, and the final output can be a mean or most frequent term from all models trained. It is also known as parallel learning, as all weak learners learn at the same time. Boosting is generally a stagewise addition method, where multiple weak learners are trained, and all the models are of the same machine learning algorithm. The errors and the mistake from the previously trained weak learner are considered to avoid the same errors in the further training of the next weak learner. It is also known as sequential learning, as the weak learner learns in sequence with each other.

bagging and boosting

We can not say which algorithm will perform better all the way, but generally, bagging is preferred when there is a low bias and high variance in the dataset (overfitting), whereas boosting is preferred in the case of a high bias and low variance dataset (underfitting).

Conclusion

This article discusses the top 5 interview questions with the core idea and intuition behind them. Reading and preparing these questions will help one understand the bagging algorithm’s core intuition and how it differs from other algorithms.

Some Key Takeaways from this article are:

1. Random forest is a bagging algorithm with decision trees as base models.

2. Bagging uses sampling of the data with replacement, whereas pasting uses sampling of the data without replacement.

3. Bagging performs well on the high variance dataset and boosting performs well on high-bias datasets.

Test your knowledge and enhance your skills with our ‘Mastering Random Forest and Bagging Techniques‘ course! Dive into challenging questions on the Random Forest algorithm and gain the confidence you need to excel in your data science career—Enroll now and take your expertise to the next level!

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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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An interesting and very informative read.

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