Abstract
To find observations that differ considerably from the bulk of the data points, outlier detection is an essential task in data analysis. To put it more simply, outliers are individual data points that stand out from the rest of the dataset. the Iris dataset, a machine learning benchmark, is used for outlier detection. The Rank SVM method, which is typically utilized for ranking jobs but has been modified for outlier identification, is used to find outliers. Standardizing the features pre-processes the dataset, which consists of measurements of iris flower sepal and petal diameters. The standardised dataset is used to train a Rank SVM model. According to the model's categorization, outliers are anticipated to be categorized as -1 and inliers as 1. The study shows the indices of outliers found in the Iris dataset and sheds light on how well Rank SVM works for outlier identification tasks. Keywords: outlier detection, iris dataset, human behavior, machine learning
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
