Top questions about Machine Learning: pt. 1

Cath Sandoval
Copywritter

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Artificial intelligence and machine learning have been technologies that many companies have bet on. They are indeed complex but offer us a lot of benefits.

In a series of 4 articles, we have compiled the most relevant questions about Machine learning.

Shall we get started?

What is machine learning?

Machine learning is a branch of computer science that deals with programming systems to automatically learn and improve with experience. 

For example: Robots are programmed to be able to perform tasks by relying on data they collect from sensors. The programs learn automatically from the data.

What is overfitting in machine learning?

In machine learning, when a statistical model describes is good at predicting seen samples, but performs very poorly on unseen samples. 

The model shows poor performance that has been overfitted.

Why does the overadjustment occur?

The possibility of overfitting is due to the fact that the criteria used to train the model are not the same as those used to judge the effectiveness of a model.

It is possible that it was forgotten to leave enough samples to test the generalization power of the model on unseen samples.

In this technique, a model is usually given a set of known data on which the training is run (training data set), and a set of unknown data against which the model is tested.

The idea of cross-validation is to define a data set to “test” the model in the training phase.

Having technology such as Machine learning will always be an opportunity for innovation on the part of insurance companies. That is why, as well as this one, we will bring you in the coming days, a series of articles where we will develop the whole topic.

You can’t miss it!

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