Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature ...

One example would be using an algorithm to sort through a set of financial transactions and picking out instances of potential fraud. Binary logistic regression can predict a binary outcome ...

Leaving out neural networks and deep learning, which require a much higher level of computing resources, the most common algorithms are Naive Bayes, Decision Tree, Logistic Regression, K-Nearest ...

A team of Master’s students in Data Science and Big Data at Hassan II University in Casablanca has designed an artificial ...

Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...

Symbolic regression algorithms are distinct from deep neural networks, the famous artificial intelligence algorithms that may take in thousands of pixels, let them percolate through a labyrinth of ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the AdaBoost.R2 algorithm for regression problems (where the goal is to predict a single numeric value). The ...

Linear regression is a simple machine learning algorithm that has many uses for analyzing data and predicting outcomes. Linear regression is especially useful when your data is neatly arranged in ...