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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
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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 Support Vector Machines

Parth Shukla
Parth Shukla Last Updated : 14 Feb, 2023
6 min read

Introduction

Support vector machines are one of the most widely used machine learning algorithms known for their accuracy and excellent performance on any dataset. SVM is one of the algorithms that people try on almost any kind of dataset, and due to the nature and working mechanism of the algorithm, it learns from the data as well, no matter how the data is and what type it is.

This article will discuss and answer the intervention on support vector machines with proper explanations and reasons behind them. This will help one to answer these questions efficiently and accurately in the interview and will also enhance the knowledge on the same.

Learning Objectives

After going through this article, you will learn.

  • Kernal tricks and margin concepts in SVM
  • A proper answer to why SVM needs longer training duration and why it is nonparametric
  • An efficient way to answer questions related to SVM
  • How interview questions can be tackled in an appropriate manner

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

Table of Contents

  1. How would you explain SVM to a nontechnical person?
  2. What are the Assumptions of SVM?
  3. Why is SVM a nonparametric algorithm?
  4. When do we consider SVM as a Parametric algorithm?
  5. What are Support vectors in SVM?
  6. What are hard and soft-margin SVMs?
  7. What are Slack variables in SVM?
  8. What could be the minimum number of support vectors in N-dimensional data?
  9. Why SVM needs a long training duration?
  10. What is the kernel trick in SVM?
  11. Conclusion

Q1. How Would You Explain SVM to a Nontechnical Person?

To explain the intuition and working mechanism of the support vector machine to a nontechnical person, I would take the example of roads.

Understanding of SVM with example

As we can see in the above image, there are a total of three lines that are present on the road; the middle line divides the route into two parts, which can be understood as a line dividing for positive and negative values, and the left and right bar are them which signifies the limit of the road, means that after this line, there will be no driving area.

Same way, the support vector machine classifies the data points which the help of regression and support vector lines, here the upper and lower or the left and suitable vectors are limited for the positive and negative values, and any data point lying after these lines are considered as a positive and negative data point.

Q2. What are the Assumptions of SVM?

There are no certain assumptions about the SVM algorithm. Instead, the algorithm learns from the data and its patterns. If any data is fed to the algorithm, the algorithm will take time to learn the patterns of the data, and then it will result accordingly to the data and its behavior.

Q3. Why Support Vector Machine is a Nonparametric Algorithm?

Nonparametric machine learning algorithms assume any assumption during the model’s training. In these types of algorithms, the model does not rely on and function that will be used during the training and testing phase of the model; instead, the model trains on the patterns of the. Instead returns an output.

In the same way, the SVM algorithm also does not assume any assumptions from the data and learns from the data observations that are fed to the algorithm. That is why the SVM is known as a nonparametric, That learning algorithm. Although due to this, there is a great advantage associated with algorithms that are the learning of the algorithm is not limited as any data can be fed to the algorithm, and the a can learn from it, unlike parametric algorithm, which can only learn from the data which satisfies, s its assumptions.

Q4. When do we consider SVM as a Parametric Algorithm?

In the case of linear SVM, the algorithm tries to fit the data linearly and produces a linear boundary to split the data; here, as the regression line or the boundary line is linear, its principle is the same as the linear regression, and hence the direct function can be applied to solve the problem, which makes the algorithm parametric.

Q5. What are Support Vectors in SVM

Support vectors in SVM are data points, or we can call them regression line, which divides or classifies the data. The data points or the observations that fall below or above the support vectors are then classified accordingly to their category.

In SVMs, the support vector is considered for classifying the data observations, and they are only responsible for the accuracy and,d the performance of the model. Here the distance between the vectors should be maximized to increase the model’s accuracy. The points should fall after the support vector; some data points can lie before or between support vectors.

Q6. What is Hard and Soft margin SVMs?

As shown in the below image, some of the data points in soft margin SVM are not precisely lying inside their margin limits. Instead, they are crossing the boundary and lying a. Instead. Rather, instead. Instead, such a distance from their respective vector line.

Decision Boundaries in SVM

Whereas the hard margin SVM are those in which the data points are restricted to lie after their respective vector and are not allowed to cross the margin limit, which can be seen in the above image.

Q7. What are Slack Variables in SVM?

Slack variables in SVM are defined in so if margin algorithm that how much a particular data observation is allowed to violet the limit of the support vector and go beyond or above it. Here note that the more the slack variable, the violation of the support vector. To get an optimum model, we need to reduce the slack variable as much as possible.

Q8. What Could be the Minimum Number of Support Vectors in “N” Dimensional Data?

To classify the data points into their respective classes, there could be a minimum of two support vectors in the algorithm. Here, the data’s time or size will not affect the number of vectors, as per the general understanding of the algorithm. Theds a minimum of two support vectors to classify the data (in case of binary classification).

Support Vectors in SVM

 

Q9. Why SVM needs a Longer Training Duration?

As we mentioned, SVMs are a nonparametric machine learning algorithm that does not rely on any specified function; instead, they learn the data patterns and then return an output. Due to this, the model needs time to analyze and sklearn from the data, unlike the parametric model, which implements the function to train on data.

The SVM provides one of the best advantages due to this, as the SVM knows to learn from the data, the learning of the algorithm is not limited, and the model can be implemented on almost any kind of data. At a given time and space complexity, the model can learn nearly any type of data pattern, unlike those parametric models whose knowledge a5re limited as they only get trained on the data, which satisfies their assumptions.

Q10. What are Kernel Tricks in SVMs?

The support vectors in SVMs are one of the best approaches to solving data patterns and can classify the linearly separable data set. Still, in the case of nonlinear data, the same decision boundary can not be used as it will perform inferior, and that is where the kernel trick comes into action.

Kernel Trick in SVM

The kernel trick allows the support vector to separate between nonlinear data classes and classify nonlinear data with the exact working mechanism.

Here, several functions are kernel tricks, and some popular kernel functions are linear, nonfunctions linear, polynomial, and sigmoid.

Conclusion

In this article, we discussed the support vector machine and some interview questions related to the same. This will help one answer these questions efficiently and correctly and enhance knowledge about this algorithm.

Some of the Key Takeaways from this article are:

  1. Support Vector Machines are one of the best-performing machine learning algorithms which use its support vector to classify the data and its classes.
  2. Complex margin support vectors do not allow data points to cross their respective vectors, whereas, in soft margin SVM, there are no complicated rules, and some of the data points travel the margin.
  3. A support vector machine is a nonparametric model that takes more time for training, but the algorithm’s learning is not limited.
  4. In the case of nonlinear data, the kernel function can be used in SVM to solve the data patterns.

Wants to contact the author?

Follow Parth Shukla @AnalyticsVidhya, LinkedIn for more content. Contact Parth Shukla @ Parth Shukla | Email to contact me.

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

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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