## Gradient boosting machine learning

Gradient boosting is a machine learning technique for regression problems, which produces a prediction model in the form of an ensemble of weak prediction models.

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Gradient boosting is a machine learning technique for regression problems, which produces a prediction model in the form of an ensemble of weak prediction models.

An overview of different ways to create graphs in Mathematica.

There are many visualizations for tree-like data but it’s more difficult to display true graph-like data in a way that one doesn’t get lost in the bifurcation of relationships and nodes. The interactive diagram below is a great way to navigate graph-data in a compact fashion and can be used in many ways; for navigating […]

This is an overview of the discrete differential calculus on graphs with an emphasis on the usage of Mathematica to perform related calculations.

An overview of the data generators inside the numerics library for .Net.

Where art and mathematics meet.

As simple as can be but many authors use incorrect terminology in deriving the Black-Scholes equation.

Simple clustering with Mathematica.

About important nodes in a network and the Katz measure.

An introduction to graphs in F#.