… This article focusses on supervised learning, a subcategory of machine learning where the
target values are known in a given dataset. Although many concepts also apply to regression …

… The purpose of evaluation in Machine Learning is to determine the usefulness of our … ) or
of our learning algorithms on various collections of data sets. Optimally, the evaluation process …

… 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 …

… We study the problem of evaluation of different classification models that are used in machine
learning. The reason of the model evaluation is to find the optimal solution from various …

… machine learning techniques to agricultural databases. W e have designed and implemented
a machine learning … dataset using a variety of machine learning schemes, and has several …

… used machine learning algorithms and discuss the applications and performance of machine
learning in surface water, groundwater, drinking water, wastewater, and seawater (Fig. 1). …

… and weaknesses of different machine learning methodologies. We … Finally, we apply a number
of established machine learning … to properly benchmark machine learning algorithms, and …

… research is based on the comparison of machine learning algorithms for some subtask, the
… in Machine Learning of Natural Language is to investigate which type of learning algorithm …

… The current heterogeneity in evaluation techniques and metrics leads to difficulty in (…
evaluation guidelines for different types of ML-based publications, emphasizing supervised learning…

… machine learning algorithms. In this paper, the following implemented the machine learning
… to using KDD dataset for implementing machine learning algorithms and shows how KDD …