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In this paper, we present a method for comparing and evaluating different collections of machine learning algorithms on the basis of a given performance measure (e.g., accuracy, area under the curve (AUC), F-score). Such a method can be used to compare standard machine learning platforms such as SAS, IBM SPSS, and Microsoft Azure ML. A recent trend in automation of machine learning is to exercise a collection of machine learning algorithms on a particular problem and then use the best performing algorithm. Thus, the proposed method can also be used to compare and evaluate different collections of algorithms for automation on a certain problem type and find the best collection. In the study reported here, we applied the method to compare six machine learning platforms – R, Python, SAS, IBM SPSS Modeler, Microsoft Azure ML, and Apache Spark ML. We compared the platforms on the basis of predictive performance on classification problems because a significant majority of the problems in machine learning are of that type. The general question that we addressed is the following: Are there platforms that are superior to others on some particular performance measure? For each platform, we used a collection of six classification algorithms from the following six families of algorithms – support vector machines, multilayer perceptrons, random forest (or variant), decision trees/gradient boosted trees, Naive Bayes/Bayesian networks, and logistic regression. We compared their performance on the basis of classification accuracy, F-score, and AUC. We used F-score and AUC measures to compare platforms on two-class problems only. For testing the platforms, we used a mix of data sets from (1) the University of California, Irvine (UCI) library, (2) the Kaggle competition library, and (3) high-dimensional gene expression problems. We performed some hyperparameter tuning on algorithms wherever possible. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0825 .

There is a need to scrutinise and retrieve information from data in today's world. Clustering is an analytical technique which involves dividing data into groups of similar objects. Every group is called a cluster, and it is formed from objects that have affinities within the cluster but are significantly different to objects in other groups. The aim of this paper is to look at and compare two different types of hierarchical clustering algorithms. Partition and hierarchical clustering are the two main types of clustering techniques. Hierarchical clustering algorithm is one of the algorithms discussed here. The aforementioned algorithms are described and analysed in terms of factors such as dataset size, data set type, number of clusters formed, consistency, accuracy, and efficiency. Hierarchical clustering is a cluster analysis technique that aims to create a hierarchy of clusters. A hierarchical clustering method is a set of simple (flat) clustering methods arranged in a tree structure. These methods create clusters by recursively partitioning the entities in a top-down or bottom-up manner. We examine and compare hierarchical clustering algorithms in this paper. The intent of discussing the various implementations of hierarchical clustering algorithms is to assist new researchers and beginners to understand how they function, so they can come up with new approaches and innovations for improvement.