Principal Component Analysis In Machine Learning


Hello everybody,

today I want to note important for me details of Machine Learning. 

So, the first and very important usage of PCA is visualizing data. If you have 10 dimensions, can you visualize those data? If you can I'm happy about you, but I can't. I can imagine only 1, 2, 3 D :). But with principal componenet analysis it's possible to visualize data. 

Second application is reducing memory/disk need to store data. That's quite self-explanatory, to train on 10 000 dimensions and 100 dimensions is different.

Third is speeding up learning algorithm. It's actually related with second.

Another important detail, it's bad idea to use PCA in order to avoid overfitting. Actually everybody who does machine learning knows that decreasing number of features increases chances of overfitting.

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