Mindful Machines

  These days there is a hype in the world that if one has more features then we have better discrimination in the model but this hypothesis will not hold in all the situations. Actually the performance of the model vs no of features will decrease when the number of features increases and it will look like As in Instance-Based learning methods (like k-means) where features are very important in the case of k-means extra features will add noise in the distance calculation. To handle this problem we should have to avoid irrelevant features while selecting the features because they will add the noise to the model learning. These extra features will generally affect the performance when we have a limited training dataset. In the data science world, we call this a  curse of dimensionality Curse of dimensionality in simple words is when we have training dataset with large number of training features and more computational resource to learn. The only solution to this big threat is fea...