As the saying goes: garbage in, garbage out. Your system will only be capable of learning if the training data contains enough relevant features and not too many irrelevant ones. A critical part of the success of a Machine Learning project is coming up with a good set of features to train on. This process, called feature engineering, involves: Feature selection: selecting the most useful features to train on among existing features. Feature extraction: combining existing features to produce a more useful one (as we saw earlier, dimensionality reduction algorithms can help). Creating new
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