… Concentrating on performance evaluation as we do in this paper, let us consider what happens
in one of the most common evaluation scenarios in machine learning: cross-validation of …

… study compares RF with other machine learning models, such as DT, SVM, LR and NN. The
experimental results illustrate that RF has the best performance on classification accuracy in …

… of predictive models based on the four machine learning methods, namely Bayes Network,
Naïve … if a particular predictive model derived from machine learning methods aligns with their …

Grouping the objects based on their similarities is an important common task in machine
learning applications. Many clustering methods have been developed, among them k-means …

… different machine learning … machine learning techniques in intrusion detection is limited.
Hence this paper aims at understanding the implications of using supervised machine learning …

… a classification and performance evaluation framework … machine learning algorithms in the
context of an object-based image analysis and to thoroughly test the algorithm’s performance …

… Most of these works are utilizing machine learning algorithm to separate spam and nonspam…
, there lacks of a performance evaluation of existing machine learning-based streaming …

… as machine learning, artificial intelligence (AI) and statistical. In this study machine learning
… The data set used in this research was collected from UCI machine learning repository which …

… of six machine learning techniques was evaluated for prediction of heart disease. The
recital of these methods was assessed on eight diverse classification performance indices. In …

… machine learning algorithms can be applied to solve this problem. In this work, popular
supervised and unsupervised machine learning … It was found that unsupervised machine learning …