This paper proposes a Bayesian model to compare multiple algorithms on multiple data sets, on any metric. The model is based on the Bradley-Terry model, that counts the number of times one algorithm performs better than another on different data sets. Because of its Bayesian foundations, the Bayesian Bradley Terry model (BBT) has different characteristics than frequentist approaches to comparing multiple algorithms on multiple data sets, such as Demsar (2006) tests on mean rank, and Benavoli et al. (2016) multiple pairwise Wilcoxon tests with p-adjustment procedures. In particular, a Bayesian approach allows for more nuanced statements regarding the algorithms beyond claiming that the difference is or it is not statistically significant. Bayesian approaches also allow to define when two algorithms are equivalent for practical purposes, or the region of practical equivalence (ROPE). Different than a Bayesian signed rank comparison procedure proposed by Benavoli et al. (2017), our approach can define a ROPE for any metric, since it is based on probability statements, and not on differences of that metric. This paper also proposes a local ROPE concept, that evaluates whether a positive difference between a mean measure across some cross validation to the mean of some other algorithms is should be really seen as the first algorithm being better than the second, based on effect sizes. This local ROPE proposal is independent of a Bayesian use, and can be used in frequentist approaches based on ranks. A R package and a Python program that implements the BBT is available.

The term machine learning refers to a computational system that has the ability to ingest data, analyze it and spot patterns and trends. Generally considered a subset of artificial intelligence (AI), machine learning (ML) systems generate algorithms based on a set of sample data and then deliver predictions, without being expressly programmed to do so. Moreover, these algorithms change and adapt as new data appears or conditions change. This autonomous learning capability is at the center of today's enterprise. It's increasingly used to make important decisions and drive automation.

Creating machine learning models is a complex process that even the most experienced data scientists often make mistakes in. If you want your machine learning models to be as accurate as possible, you need to be aware of the ways that you can improve them. In this post, we will discuss five ways to improve the accuracy of your machine learning models! One of the easiest ways to improve the accuracy of your machine learning models is to handle missing values and outliers. If you have data that is missing values or contains outliers, your models will likely be less accurate.

Over the years, a number of search algorithms have been proposed in AI literature, ranging from best-first to depth-first searches, from incomplete to optimal searches, from linear memory to unbounded memory searches; each having their strengths and weaknesses. The variability in performance of these algorithms makes algorithm selection a hard problem, especially for performance critical domains. Algorithm portfolios alleviate this problem by simultaneously running multiple algorithms to solve a given problem instance, exploiting their diversity. In general, the portfolio methods do not share information among candidate algorithms. Our work is based on the observation that if the algorithms within a portfolio can share information, it may significantly enhance the performance, as one algorithm can now utilize partial results computed by other algorithms. To this end, we introduce a new search framework, called Search Portfolio with Sharing (SP-S), which uses multiple algorithms to explore a given state-space in an integrated manner, seamlessly combining the partial solutions, while preserving the constraints/characteristics of the candidate algorithms. In addition, SP-S can be easily adopted to guarantee theoretical properties like completeness, bounded sub-optimality, and bounded re-expansions. We describe the basics of the SP-S framework and explain how different classes of search algorithms can be integrated in SP-S. We discuss its theoretical properties and present experimental results for multiple domains, demonstrating the utility of such a shared approach.

According to Gartner's hype cycle of emerging technologies, 2017; Deep Learning and Machine Learning have reached the peak of inflated expectations. Artificial General Intelligence (AGI) and Deep Reinforcement Learning are in the phase of innovation trigger. The sentiment over Artificial Intelligence (AI) is euphoric. Every technology firm is jumping on the AI first bandwagon. Companies like Google, Microsoft, Amazon, and Alibaba are pushing the frontiers.

With the rise of automation in nearly every industry, there is still a considerable debate on the nature of jobs responsible for the automation. Jobs can vary from linguistics in natural language processing, predictive modeling in data mining to software engineers in self-driving cars. However, there has to be some underlying distinction between the jobs, at least as far as machine learning (ML) is concerned. In simple terms, it is a process of training a system to perform a task without describing how it should perform the task. A more technical definition would be: "… a machine learns with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at task T, following experience E." 1 This involves taking a series of inputs, feeding them into a system, and allowing a system to learn what is a desired output.