Approximation Or Classification?

A blog post on the Algolytics blog discusses different approximation and classification models and when to use each:

Even if your target variable is a numeric one, sometimes it’s better to use classification methods instead of approximation ones. For instance if you have mostly zero target values and just a few non-zero values. Change the latter to 1, in this case you’ll have two categories: 1 (positive value of your target variable ) and 0. You can also split numerical variable into multiple subgroups : apartment prices for low, medium and high by equal subset width and predict them using classification algorithms. This process is called discretization.

Both types of models are common in machine learning, so a good understanding of when to use which is important.

Related Posts

Explaining Neural Networks With H2O

Shirin Glander explains some of the concepts behind neural networks using H2O as a guide: Before, when describing the simple perceptron, I said that a result is calculated in a neuron, e.g. by summing up all the incoming data multiplied by weights. However, this has one big disadvantage: such an approach would only enable our neural net […]

Read More

Azure ML Studio Supports R 3.4

David Smith notes that Azure ML Studio now supports R version 3.4: With the Execute R Script module you can immediately use more than 650 R packages which come preinstalled in the Azure ML Studio environment. You can also use other R packages (including packages not on CRAN) and source in R scripts you develop elsewhere (as […]

Read More


July 2016
« Jun Aug »