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Why Data Preparation Is So Important in Machine Learning
by Jason Brownlee on June 15, 2020 in Data Preparation

Photo by Markus Spiske on Unsplash

On a predictive modeling project, machine learning algorithms learn a mapping from input variables to a target variable.

The most common form of predictive modeling project involves so-called structured data or tabular data. This is data as it looks in a spreadsheet or a matrix, with rows of examples and columns of features for each example.

We cannot fit and evaluate machine learning algorithms on raw data; instead, we must transform the data to meet the requirements of individual machine learning algorithms. More than that, we must choose a representation for the data that best exposes the unknown underlying structure of the prediction problem to the learning algorithms in order to get the best performance given our available resources on a predictive modeling project.

Given that we have standard implementations of highly parameterized machine learning algorithms in open source libraries, fitting models has become routine. As such, the most challenging part of each predictive modeling project is how to prepare the one thing that is unique to the project: the data used for modeling.


⭐️ NASA – Best Photo from Last Week

Hubble Glimpses a Galaxy Among Many
Last Updated: June 12, 2020, Editor: Rob Garner

Looking deep into the universe, the NASA/ESA Hubble Space Telescope catches a passing glimpse of the numerous arm-like structures that sweep around this barred spiral galaxy, known as NGC 2608. Appearing as a slightly stretched, smaller version of our Milky Way, the peppered blue and red spiral arms are anchored together by the prominent horizontal central bar of the galaxy.

In Hubble photos like this, bright foreground stars in the Milky Way will sometimes appear as pinpoints of light with prominent light flares known as diffraction spikes, an effect of the telescope optics. A star with these features is seen in the lower right corner of the image, and another can be spotted just above the pale center of the galaxy. The majority of the fainter points around NGC 2608, however, lack these features, and upon closer inspection they are revealed to be thousands of distant galaxies. NGC 2608 is just one among an uncountable number of kindred structures.

Similar expanses of galaxies can be observed in other Hubble images such as the Hubble Deep Field, which recorded over 3,000 galaxies in one field of view.

Text credit: ESA (European Space Agency)
Image credit: ESA/Hubble & NASA, A. Riess et al.

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