Comparison of the performance of GaussianNB Algorithm, the K Neighbors Classifier Algorithm, the Logistic Regression Algorithm, the Linear Discriminant Analysis Algorithm, and the Decision Tree Classifier Algorithm on same dataset

Abstract

Most educational institutions worldwide have been closed since March 2020 in an effort to slow the spread of the Covid-19 epidemic. More than 90% of students around the world have been influenced by this. In this study, we\'ll make a prediction about whether or not the Covid-19 epidemic has benefited student performance. Our data will be divided into training and testing datasets, with 80% of the data utilised for training and 20% for testing. To calculate the accuracy of our predictions, we\'ll use six different algorithms, including the RandomForestClassifier Algorithm, the GaussianNB Algorithm, the K Neighbors Classifier Algorithm, the Logistic Regression Algorithm, the Linear Discriminant Analysis Algorithm, and the DecisionTree Classifier Algorithm.

Introduction

I. INTRODUCTION

A. RandomForestClassifier: Suitable for Binary, Continuous and categorical data type.

The Random Forest Algorithm consists of several decision trees on various subsets of a given dataset. Based on the concept of ensemble learning process, it creates decision trees based on data samples. It gets the prediction from each of them and selects the best solution by means of voting.

1. Advantages

• Reduces risk of over fitting and the required training time.
• Runs efficiently in large database while producing highly accurate predictions by estimating missing data.

2. Disadvantages

• Compared to a decision tree, it is slower.
• It requires significant memory for storage due to the need for retaining the information from several hundred individual trees.

B. LogisticRegression: Suitable for Binary data type.

Logistical Regression is a statistical method used for building machine learning models. It is considered as one of the simplest Machine Learning Algorithm which can be applied on various classification problems such as Spam Detection, Diabetes, Prediction, Cancer detection etc. There are only two possible outcomes in a logistic Regression formula(Dichotomous).

1. Advantages

• No problems scaling input features and does not require tuning.
• It is highly interpretable and does not require too many computational resources.
• Easy to implement and train a model using Logistic Regression.

2. Disadvantages

• Constructs Linear Boundaries.
• It’s inefficient when the number of observations are lesser than the number of features. Which can lead to overfitting.

C. LinearDiscriminantAnalysis: Suitable for Independent variables

The LDA is a supervised algorithm that aims to find the linear discriminant to represent the axes that maximize separation between different classes of data. This reduces the number of features which reduces the computing cost significantly (similar to principal component analysis). It is used in face recognition, prediction, customer identification, medical fields etc.

1. Advantages

• It is a simple, fast and portable algorithm.
• It can beat Linear Regression when its assumptions are met.

2. Disadvantages

• Requires normal distribution assumption on features/predictors.
• Sometimes not good for few categories variables.

D. KNeighbours: Suitable for Individual Data.

The K nearest Neighbour is useful when you are performing a pattern recognition test. It classifies a data point based on it's neighbor's classification and stores all available cases. Although it is mostly recommended for Classification problems, it can also be used for regression. The algorithm is Non-Parametric, which means it does not make any assumption on underlying data.

1. Advantages

• Simple to implement with just one parameter.
• It is more effective if training data is large.
• Used for Classification, Regression, Ranking, Recommendations, imputations etc.

2. Disadvantages

• The Value of k must always be determined which can be complex.
• Calculating the distance between the data points for all the training samples results in a higher computation cost.
• Size of the model grows with new data incorporated.
• It is a distance based-approach hence the model can be badly affected by outliers. Making it prone to Overfitting.

E. DecisionTree

A decision tree is a graphical representation for getting all the possible solutions to a problem/decision based on given conditions. They can be used in both regression and classification tasks. A decision tree comprises of two nodes, a decision node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf Nodes are the output of these decisions.

1. Advantages

• Decision Trees mimics the human thinking ability while making a decision, which makes it easy to understand/interpret.
• There is less requirement of data cleaning compared to other algorithms.

2. Disadvantages

• The Decision tree contains lots of layers, which makes it complex.
• It may have Overfitting Issues.

F. GaussianNB: Suitable for Continuous Data

Gaussian Naive Bayes is a variant of Naïve Bayes that follows Gaussian normal distribution and supports Continuous data. The Gaussian or Normal distribution is the simplest to implement as the user is required to calculate the mean and standard deviation for the training data. It overall provides better performance by eliminating insignificant specifications. The algorithm uses probability for many classification functions while other functions are used to estimate data distribution.

1. Advantages

• Fast and Flexible Model that works well with Large data.
• No need to spend much time for training.

2. Disadvantages

• Large data records are required to achieve good results.
• Shows lower performance than the other classifiers according to the type of problem.

II. RESEARCH METHODOLOGY

A survey was given to Goa students in various classrooms via online Google forms in order to get information on the learning curve of students during COVID-19. There were options to choose from when answering the survey's questions. The best choice that reflected their values had to be selected by the pupils. This allowed us to collect responses from around the State by disseminating the poll from September 30 to October 10, 2021. After reviewing the data, we chose approximately 461 records with the intention of obtaining a wide range of replies.

A. Data Collection

We gathered the information by sending out questionnaires via Google Forms to our Goa-based students, friends, family, and other well-wishers.

B. Data Representation

Email address, name, educational level, name of the institution, age, gender, taluka, and a few other columns have sub-questions make up the total of 36 columns, including the timestamp which is the default.

C. Data Preprocessing and Cleaning

Excel was utilised to pre-process the data, and a Jupyter notebook was used for analysis and analytics. You may create and share documents with live code, equations, visualisations, and text with this open source web application.

We used a variety of steps for pre-processing.

First, we converted any suitable string values to numeric numbers. The data was then filtered, and outliers were eliminated from the necessary rows. This decreased the number of rows from 561 to 423 rows. We then generated 5 graphs using the filtered data that were required for further investigation.

D. Data Analysis

After cleansing and preprocessing the data, feature selection was done. To achieve the best accuracy, we took 22 columns out of 35 columns. Then, 2 columns—Sum and Final Result—were added. The sum column contains the total count for each row that was calculated, and the final result column contains the average of all responses for each individual. All of this was done using simply Excel, where 0 means performance has not increased and 1 means performance has.

E. Data Analytics

To achieve the best accuracy, we used feature selection on our dataset and removed 22 of the dataset's 35 columns. The data was then divided into training and testing. The Random Forest Classifier technique was then used to create baseline models, and five other algorithms were employed to assess the accuracy.

III. RESULTS AND ANALYSIS

A. Data analysis

The pupils were given a multiple-choice grid with five distinct options for each statement (only one of these could be selected for each statement). Agree, Disagree, Neutral, Strongly Agree, and Strongly Disagree were the available responses for each statement. The statements were categorised into five main groups: the ability to adapt to online classes, problems with online classes, mental health problems caused by online classes, the effectiveness of the online teaching and learning process, and the overall effect on personality.

1)  Adaptability To Online Classes

50% of students believe that e-learning tools are easy to use and 50% say they can do tasks faster in online mode. Most of the students strongly believe that online classes are not better than normal classroom classes. 70%  agree that teachers are putting lots of efforts into making it easier for students to learn online

The accuracy of the GaussianNB() algorithms has increased when compared to the LinearDiscriminantAnalysis() algorithm, but it has decreased (by 95.29%) when compared to the RandomForestClassifier() algorithm, LogisticRegression() algorithm, K NeighborsClassifier() algorithm, and DecisionTreeClassifier() algorithm, all of which have accuracy scores of 100%..

Conclusion

TABLE I ALGORITHM ACCURACY SR.NO. ALGORITHMS ACCURACY 1 RandomForestClassifier() Algorithm 100% 2 LogisticRegression() Algorithm 100% 3 LinearDiscriminantAnalysis() Algorithm 94.11% 4 KNeighboursClassifier() Algorithm 100% 5 DecisionTreeClassifier() Algorithm 100% 6 GaussianNB() Algorithm 95.29% According to the preceding table, all four techniques, with the exception of LinearDiscriminantAnalysis() and GaussianNB(), provide 100% accuracy. 354 students\' grades have increased, whereas 69 students\' grades have not.

References

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Copyright

Copyright © 2022 Nisha Sawant, Dnyandev Ravindra Khadapkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.