Too Many Features? Let’s Look at Principal Component Analysis

Home-brewed machine learning model series

The companion repo is available here!

The curse of dimensionality is one major problem in machine learning. As the number of features increases, so does the complexity of the model. Moreover, if there is not enough training data, it results in overfitting.

In this entry, Principal Component Analysis (PCA) will be introduced. First, I will explain why too many features are a problem. Then, the math behind PCA and why it works. Additionally, PCA …

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