How ML models handle noise

Reducing noise from datasets is a time-consuming task, and it is also a task that cannot be easily automated. We need to understand whether we have noise in the data, what kind of noise is in the data, and how to remove it. Luckily, most machine learning algorithms are pretty good at handling noise.

For example, the algorithm that we have used quite a lot so far – random forest – is quite robust to noise in datasets. Random forest is an ensemble model, which means that it is composed of several separate decision trees that internally “vote” for the best result. This voting process can therefore filter out noise and coalescence toward the pattern contained in the data.

Deep learning algorithms have similar properties too – by utilizing a number of small neurons, these networks are robust to noise in large datasets. They can coerce the pattern in the data.

Best practice #33

In large-scale software systems, if possible...