Overfitting is a very comon problem in machine learning. It occurs when your model starts to fit too closely with the training data. In this article I explain how to avoid overfitting.
Overfitting is the data scientist’s haunt. Before explaining what are the methods that we can use to overcome overfitting, let’s see how to detect it.
In data science, perfect data do not exist. You always have noise and inaccuracies. A model overfits when it starts to learn this noise. The result is a biased model that you can’t generalize.
In practice, a model that overfits is often very easy to detect. Overfitting occurs when the error on the testing dataset start increasing. Typically, if the error on the training data is too much smaller than the error on the testing dataset, your model may have learned too much. …
No, it’s not about Terminator…
A lot is said about machine learning. Some perceive it as a monster that is leading humanity to its doom. Others perceive it as a magician who solves all their illness. In reality it’s much simpler than that (and a little bit less scary).
Machine learning is a technique that allows automatic systems improvement using data.
The amount of data explosion and the progress in processing and storage techniques, have help machine learning to establish in many areas.
Behind this mysterious name hides a very simple concept. To learn, the system is inspired by existing samples, grouped in databases. …