Getting to know your data

For many years, researchers argued about what is more important: data or algorithms. But now, it looks like the importance of data over algorithms is generally accepted among ML specialists. In most cases, we can assume that the one who has better data usually beats those with more advanced algorithms. Garbage in, garbage out—this rule holds true in ML more than anywhere else. To succeed in this domain, one need not only have data, but also needs to know his data and know what to do with it.

ML datasets are usually composed from individual observations, called samples, cases, or data points. In the simplest case, each sample has several features.