# Guide to multi-class multi-label classification with neural networks in python

Often in machine learning tasks, you have multiple possible labels for one sample that are not mutually exclusive. This is called a multi-class, multi-label classification problem. Obvious suspects are image classification and text classification, where a document can have multiple topics. Both of these tasks are well tackled by neural networks. A famous python framework for working with neural networks is keras. We will discuss how to use keras to solve this problem.

# Getting started with Multivariate Adaptive Regression Splines

In this post we will introduce multivariate adaptive regression splines model (MARS) using python. This is a regression model that can be seen as a non-parametric extension of the standard linear model. The multivariate adaptive regression splines model MARS builds a model of the from $$f(x) = \sum_{i=0}^k c_i B_i(x_i),$$ where $x$ is a sample vector, $B_i$ is a function from a set of basis functions (later called terms) and $c_i$ the associated coefficient.