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Feature selection is important for improving model accuracy from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Feature selection is crucial for improving model accuracy. Not all features are equally valuable for prediction, and some may even introduce noise. Including irrelevant or redundant features can lead to overfitting, where the model performs well on the training data but poorly on unseen data. Feature selection helps in identifying the most relevant features that contribute the most to prediction accuracy. By selecting the right features, a data scientist can simplify the model and reduce complexity, leading to better performance. Irrelevant features can confuse the model and make it harder to interpret the results. Therefore, selecting the most important features can make the model ...
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