Overfitting and Underfitting With Machine Learning Algorithms

Last Updated on August 12, 2019

The cause of poor performance in machine learning is either overfitting or underfitting the data.

In this post, you will discover the concept of generalization in machine learning and the problems of overfitting and underfitting that go along with it.

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Approximate a Target Function in Machine Learning

Supervised machine learning is best understood as approximating a target function (f) that maps input variables (X) to an output variable (Y).

Y = f(X)

This characterization describes the range of classification and prediction problems and the machine algorithms that can be used to address them.

An important consideration in learning the target function from the training data is how well the model generalizes to new data. Generalization is important because the data we collect is only
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