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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 Interview Questions on Voting Ensembles in Machine Learning

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

Voting ensembles are the ensemble machine learning technique, one of the top-performing models among all machine learning algorithms. As voting ensembles are the most used ensemble techniques, there are lots of interview questions related to this topic that are asked in data science interviews.

This article will explore and answer the top interview questions with their core intuition and working mechanisms with code examples. Reading and practising these questions will help one answer the interview questions related to the voting ensembles very efficiently.

Read about different ensemble methods here.

Voting Ensembles

Let’s start solving them one by one.

Interview Questions on Voting Ensembles

1. What is Voting Ensemble? How does it work?

Voting ensembles are the type of machine learning algorithms that falls under the ensemble techniques. As they are one of the ensemble algorithms, they use multiple models to train on the dataset and for predictions.

They are two categories of voting ensembles.

  • Classification
  • Regression

Voting Classifiers are the ensembles used in classification tasks in machine learning. In Voting Classifiers, multiple models of the different machine learning algorithms are present, to whom the whole dataset is fed, and every algorithm will predict once trained on the data. Once all the models predict the sample data, the most frequent strategy is used to get the final prediction from the model. Here, the category most predicted by the multiple algorithms will be treated as the final prediction of the model.

For Example, if three models predict YES and two models predict NO, YES would be considered the final prediction of the model.

Voting Ensembles

Voting Regressors are the same as voting classifiers. Still, they are used on regression problems, and the final output from this model is the mean of the prediction of all individual models.

For Example, if the outputs from the three models are 5,10, and 15, then the final result would be the mean of these values, which is 15.

2. How can Voting Ensemble be Implemented?

Several ways are used in which the voting ensemble can be Implemented.

Classification

For Classification Problem, the VotingClasssifier class can be used directly for implementation, and also there is a manual method for implementing the same.

Using VotingClassifier class from sklearn:

#Using VotingClassifier
from sklearn.ensemble import VotingClassifier
model1 = LogisticRegression(random_state=7)
model2 = tree.DecisionTreeClassifier(random_state=7)
model = VotingClassifier(estimators=[('lr', model1), ('dt', model2)])
model.fit(x_train,y_train)

Regression

In regression, there are two methods to implement the voting ensemble.

  1. Using VotingRegressor Class
  2. Manual Implementation

1. Using VotingRegressor Class:

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import VotingRegressor
from sklearn.neighbors import KNeighborsRegressor
r1 = LinearRegression()
r2 = RandomForestRegressor(n_estimators=10, random_state=1)
r3 = KNeighborsRegressor()
rer = VotingRegressor([('lr', r1), ('rf', r2), ('r3', r3)])

2. Manual Implementation:

Normal Averaging:

model1 = tree.DecisionTreeClassifier()
model2 = KNeighborsClassifier()
model3= LogisticRegression()

model1.fit(x_train,y_train)
model2.fit(x_train,y_train)
model3.fit(x_train,y_train)

pred1=model1.predict_proba(x_test)
pred2=model2.predict_proba(x_test)
pred3=model3.predict_proba(x_test)

finalpred=(pred1+pred2+pred3)/3

Weighted Averaging:

model1 = tree.DecisionTreeClassifier()
model2 = KNeighborsClassifier()
model3= LogisticRegression()

model1.fit(x_train,y_train)
model2.fit(x_train,y_train)
model3.fit(x_train,y_train)

pred1=model1.predict_proba(x_test)
pred2=model2.predict_proba(x_test)
pred3=model3.predict_proba(x_test)

finalpred=(pred1*0.3+pred2*0.3+pred3*0.4)

3. What is the reason behind the better performance of the voting ensembles?

There are multiple reasons behind the performance of the voting ensembles. The primary reasons behind the excellent performance of the voting ensembles are listed below.1. If there are weak machine learning algorithms as base models, still the voting ensembles work well as there will be a power of multiple algorithms combined.

2. In Voting Classifier there are different machine learning algorithms are used. Some machine learning algorithms perform so well on some types of data. (e.g. some of them are robust to outliers etc.). So as there are multiple models involved in this process, knowledge of every algorithm will be used to solve the different problems and patterns of the dataset, and every algorithm will try to solve the design of the data. So multiple issues and practices of the data can be learned using different algorithms in voting ensembles.

3. There is also the option of manually giving weightage to the particular models. Let’s suppose we have a dataset having an outlier, so in this case, we can manually increase the weightage of the algorithm, which performs better on outliers, so ultimately, this helps the voting ensemble perform better.

4. What are hard voting and soft voting?

Hard Voting in voting ensembles, the prediction predicted from most algorithms, served as the final output. For Example, if the maximum of the base models indicates YES as output, then the final product would also be YES.

Voting Ensembles

Soft voting is the process in which the probability of every prediction is considered, and the class with the highest total chance from all base models is regarded as the final output from the model. For Example, If the possibility of the category YES as a prediction for every base model is higher than category NO, then YES would be the final prediction from the model.

5. How are voting ensembles different from other ensemble techniques?

The significant difference between voting ensembles and the other boosting algorithms are discussed below:

Voting ensembles are the ensembles technique that trains the multiple machine learning models, and then predictions from all the individual models are combined for output. Unlike voting ensembles, the same algorithm is used as base models in techniques like bagging and boosting. Also, the weights and aggregations steps are performed here, unlike voting ensembles. While stacking and blending are ensemble techniques that contain layers of algorithms, e.g. base models and meta-models. Where base models are trained, the meta-model assigns the weights to the particular base models for better algorithm performance.

Conclusion

This article discusses the top 5 interview questions related to the Voting Ensembles with the core idea and intuition behind them. Reading and practising these questions will help one understand the mechanism behind voting ensembles and help answer effectively in interviews.

Some Key Insights from this article are:

1. Voting Ensembles are ensemble techniques that perform well with weak machine-learning algorithms. Also, in the end, every individual algorithm will have combined power.

2.  They are faster than other ensemble techniques as less computational power is involved.

3. The Soft Voting technique can be used when we want to consider the weight or probability of every category in the output column.

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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