In our previous article, we discussed feature selection based on recursive elimination using sklearn. We can also select features based on model performance. For example, in the case of linear regression, we can select features and based on that calculate regression...
What is Recursive Feature Elimination (RFE)? In our previous articles, we discussed feature selection based on variance and correlation. These feature selection methods do not consider the output labels while selecting the features. As a result, these methods do not...
If two or more features in a dataset are strongly correlated, then we should select one of them. Selecting the right features for a machine learning model not only improves the performance of the model but also can reduce the training time of the algorithm. Whether...
What is feature selection in machine learning and why do we need that? A dataset may contain many columns, where each column corresponds to a feature or attribute. Now, if we run a machine learning model on all those features, we may not get optimal performance....
Sometimes we need to binarize data in a dataset. We need to transform data in a dataset in such a way that data above a specific threshold should be marked 1, and below the threshold should be marked zero. We can use the following Python code to perform binarization...
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