Now, to be clear, data scientists come in many shapes and sizes — quantitative, operational, marketing, product, predictive modelers, statisticians, engineers, miners, visualizers, warehousers, machine learners and so on — and they perform many different roles within an organization.
The archetypal data-renaissance man is mathematician, statistician, computer scientist, machine learner, and engineer all rolled into one.
Most machine learners focus on classification and do not explicitly assess the likelihood of correctness for their class predictions, unless additional analysis is performed.
Currently, MDR, implemented in Java, interfaces, at certain levels, with Weka, a popular data mining tool [ 12] in order to facilitate valid comparisons of results obtained from MDR and other machine learners provided by Weka.
The other five machine learners are ZeroR, C4.5, Bayes, k-NN and SVM [ 2, 12, 17, 18].
The AI-based models used for prediction included single and ensemble models constructed from four well-known machine learners including artificial neural networks (ANNs), support vector regression/machines (SVR/SVMs), classification and regression tree (CART), and linear regression (LR).
The machine learners had access to over a hundred descriptors for each compound, essentially infallible memory, and the ability to implement intricately designed algorithmic procedures with fast and precise numerical calculations.
Machine learners try to do this on a grander scale, seeing, for example, millions of handwritten digits, and making guesses about which digits looks more like one another, "clustering" them together based on similarity.
We propose using computational teaching algorithms to improve human teaching for machine learners.
We investigate example sequences produced naturally by human teachers and find that humans often do not spontaneously generate optimal teaching sequences for arbitrary machine learners.
The goodness of the data representation has a large impact on the performance of machine learners on the data: a poor data representation is likely to reduce the performance of even an advanced, complex machine learner, while a good data representation can lead to high performance for a relatively simpler machine learner.
