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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

Top 5 Interview Questions on Missing Value Imputation

Parth Shukla
Parth Shukla Last Updated : 12 Oct, 2024
5 min read

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

Introduction

Missing values are the type of data that contains NaN or no value for the particular record of the dataset. Handling missing values is one of the most important and tricky parts of data cleaning and preprocessing in machine learning. Most machine learning algorithms do not perform well when there is a missing value in the dataset.

In this type of situation, there are only 2 ways to solve the problem:

  • Remove the samples which are containing missing values
  • Impute the missing values with any strategy

Removing the missing values every time is not a better approach to dealing with machine learning problems, as also it can contain some useful information. So the best approach would be to imputing the missing values.

As missing values imputation is a necessary step for every type of machine learning problem, there is a very high chance of missing values that can be present in real-time datasets. In this article, we will discuss the top 5 interview questions related to the missing data imputation in machine learning with their core intuition and working mechanism behind them. Let’s start exploring and solving the interview questions one by one.

Missing Value Imputation
Source: https://resources.biginterview.com/wp-content/uploads/2022/07/Panel-Interview-101-1080×675.jpg

Missing Value Imputation Interview Questions

1. What is Complete Case Analysis (CCA) in Machine Learning?

In Machine Learning, Complete Case Analysis is a technique in which all the samples containing missing values are dropped or removed from the dataset.

As Imputation of the missing values consumes so much computational power and time, sometimes, when there is a complexity of time we can use this method as it is easy to code and faster.

#importing required libraries
import numpy as np
import pandas as pd
# importing the dataset
df= pd.read_csv('Iris.csv')
# to kno how many samples are containing missing values in it
print(df.isnull().sum())
# dropping the missing data
df = df.dropna()

Although, Complete case analysis is not the best solution for handling the missing data, as by dropping the missing data, we are also losing some of the information of the data, and also it might be possible that sometimes the dropped missing data could also contain a piece of important information that the other data does not. So in most cases, while handling the missing data, complete case analysis is not preferred unless and until there is not any other option.

According to the researcher, these techniques should be considered when there is 5% or less than 5% of the data is missing from the dataset.

2. Which Imputation is better for numerical data with outliers, mean or median? What is the reason behind them?

Most of the time, when there is a missing value in numerical data, mean and median imputation is preferred the most. Where mean imputation imputes the values by the mean of the particular column and median imputation imputes the missing values by the median values of the column data.

When an outlier is present in the dataset, median imputation is preferred the most. As Mean imputation imputes the missing values by the mean value of the column, in case of an outlier, it will count the mean also considering the outlier values, so the mean of the particular column will be biased. Whereas in the case of median imputation, it counts the median of the column, so there will not be much effect of the outliers. Hence, median imputation is preferred for numerical data having outliers.

3. What is the difference between Univariate and Multivariate Imputation of the missing data? Give Examples.

There are basically two types of missing values imputations: univariate and bivariate imputations. In univariate missing values imputations, the missing values are imputed using only the particular column of the respective missing values.

For Example, if there is a missing value present in the Age column, then the missing value will only be imputed using the values of the Age column, Any of the other columns of the dataset will not be used for the imputation.

Whereas multivariate imputations are the type of imputation in which the missing value is imputed using the values of the multiple columns in the dataset.

For Example, if a missing value is present in the Age column, then this missing value will be imputed using the Age column and some other columns of the dataset.

Mean, median, and Most_Frequent are examples of univariate missing data imputation, whereas KNN Imputer and Iterative Imputer are examples of multivariate imputation.

4. What are KNN Imputer and Iterative Imputer? How they are different from each other?

KNN Imputer is a multivariate imputation technique for imputing the missing values in the dataset. Here the euclidian distance between the data points is calculated and depending upon the euclidian distance between points, the neighbors are considered for imputing the missing data. Once we have the neighbors of the missing data based on the euclidian distance, then we calculate the mean of the neighbor’s datapoint values and impute the missing value by the mean.

The formula for the euclidian distance is:

euclidian distance

In Iterative Imputer, a simple machine learning algorithm is used for Imputing the missing values of the dataset. Here in the first step all the missing data are imputed by the mean of the respective columns, and then by moving left to the right, only one sample imputed with mean is again considered as missing values and a part from the dataset is considered as training, and testing set with dependent and independent features. In the last step, a machine learning algorithm is used to train on the dummy dataset, and the missing value is imputed by the algorithm’s results.

Missing Value Imputation
Source: https://i.ytimg.com/vi/zX-pacwVyvU/maxresdefault.jpg

5. What are the assumptions of KNN and Iterative Imputer? In which type of cases are they preferred?

KNN Imputer is a multivariate imputation technique used for missing data imputation. This technique involves the calculation of the euclidian distance in it. So it requires a higher amount of computation power as compared to the normal univariate imputation.

Iterative Imputer is also a multivariate imputation technique that involves the machine learning algorithm calculation in it. So Iterative Imputer is the imputation technique that requires the most time and computation power compared to the other imputation techniques. Also, it is slow but gives accurate results. One assumption for Iterative Imputer is that this technique is best suitable for the data missing at random.

Conclusion

In this article, we discussed the top 5 interview questions which can be asked related to the missing data imputation in machine learning and discussed each and every interview questions with its best possible answer and core intuition behind it. Practicing these interview questions will help one to understand the working method behind each technique better and also help one to answer the questions related to these topics effectively.

Some Key takeaways from this article are:

1. Complete Case Analysis is a technique that drops the missing data. It is not preferred the most, as deleting the missing values from the data is not the best suitable approach.

2. Mean imputation is not preferred most when an outlier is present in the dataset; use median imputation instead.

3.  KNN and Iterative Imputers are multivariate imputation techniques that involve higher computation. Iterative Imputer performs exceptionally well on the data missing at random.

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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