Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms (original) (raw)

Classification of Iris Flower Dataset using Different Algorithms

International Journal of Scientific Research in Mathematical and Statistical Sciences, 2022

The Iris dataset is one of the most famous dataset containing data on four attributes named as Sepal.length, Sepal.width, Petal.length, Petal.width and three classes or subspecies named as Sentosa,Versicolor and Virginic each class has 50 samples. The measurement of four attributes in CM (centimeters). This data set was developed by Ronald Fisher in 1936. This is available on UCI data set. In this study we want to show that how to solve the classification problem using some algorithms like K-means clustering, Random Forest decision, SVM, Logistic Regression, KNN, K-medoids. In addition, we also worked on four features to a advanced feature. The scikit tool we use for implementation. In this study applies classification and regression algorithms on the iris dataset by discovering and analyzing the patterns.

IRIS Species Predictor

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

In Machine Learning, we are using semi-automated extraction of knowledge of data for identifying IRIS flower species. Classification is a supervised learning in which the response is categorical that is its values are in finite unordered set. To simply the problem of classification, scikit learn tools have been used. This paper focuses on IRIS flower classification using Machine Learning with scikit tools. Here the problem concerns the identification of IRIS flower species on the basis of flowers attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS flower and how the prediction was made from analyzing the pattern to from the class of IRIS flower. In this paper we train the machine learning model with data and when unseen data is discovered the predictive model predicts the species using what it has been learnt from the trained data.

IDENTIFICATION OF DIFFERENT SPECIES OF IRIS FLOWER USING MACHINE LEARNING ALGORITHMS

IRJET, 2022

The diversity of life on earth is incredibly rich. It is very challenging to pinpoint any species due to the fact that some flower species share the same shape, size, and colour on a physical level. Similar to this, there are three subspecies of the iris flower: Versicolor, Setosa and Virginica. The Iris dataset is what we are using. There are three classes with a total of 50 occurrences in the dataset for iris flowers. Machine learning is used to distinguish between Iris flower subclasses in the Iris dataset. The study concentrates on how Machine Learning algorithms can quickly and accurately identify the class of flower rather than relying just on approximations.

IJERT-Flower Classification using Supervised Learning

International Journal of Engineering Research and Technology (IJERT), 2020

https://www.ijert.org/flower-classification-using-supervised-learning https://www.ijert.org/research/flower-classification-using-supervised-learning-IJERTV9IS050582.pdf Biodiversity of earth is very rich. About 360000 create a healthy biome within the environment of earth. Some of them are identical in physical appearance like shape, size and color. Hence it is difficult to recognize any species. Similarly Iris flower species has three subspecies Setosa, Versicolor and Virginica. We are using Iris dataset because it is frequently available. The dataset of Iris flower contains 3 classes of 50 instances each. With the help of Machine learning, Iris dataset identifies the sub classes of Iris flower. The paper focuses on how Machine Learning algorithms can automatically recognize the class of flower with the help of high degree of accuracy rather than approximately. There are three phases to implement this approach are segmentation, feature extraction and classification. Using Neural Network, Logistic Regression, Support Vector Machine and k-Nearest Neighbors

Machine Learning Classifiers Based Classification For IRIS Recognition

Qubahan Academic Journal, 2021

Classification is the most widely applied machine learning problem today, with implementations in face recognition, flower classification, clustering, and other fields. The goal of this paper is to organize and identify a set of data objects. The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. The results of the comparison analysis showed that the K-nearest neighbors outperformed the other classifiers. Also, the random forest classifier worked better than the decision tree (j48). Finally, the best result obtained by this study is 100% and there is no error rate for the classifier that was obtained.

A New Model for Iris Classification Based on Naïve Bayes Grid Parameters Optimization

2018

Data mining classification plays an important role in the prediction of outcomes. One of the outstanding classifications methods in data mining is Naive Bayes Classification (NBC). It is capable of envisaging results and mostly effective than other classification methods. Many Naive Bayes classification method provide low performance in classification and regression problems Ones of the facts behinds the performances of the NBC is dues to the assumptions of contingent on independence amidst predictors and the initials hyper parameters. However, this strong assumption leads to loss of accuracy. In this study, a new method for boosting the accuracy of NBC was proposed. The proposed new technique uses a grid search to give better accuracy Naïve Bayes classification.