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...
What is a vector norm? The length of a vector is referred to as the vector’s magnitude or the vector norm. There can be several types of norms. The vector norm |x|p is defined as: $latex {|x|}_{p}=(\sum_{i}{|x_{i}|}^{p})^{1/p}\ for\ p=1,2,3,... $ For p = 1, we get L1...
In the vector unit length scaling, a feature vector is divided by the Manhattan distance (l1 norm) or by the Euclidean distance (l2 norm). We can use the Normalizer class from the sklearn.preprocessing module to perform vector unit length scaling. Let’s look at an...
What is Robust Scaler? Let’s say there is a numerical column in a dataset. And there are outliers in the column. In that case, we can use a robust scaler to scale the features. Robust scaler uses statistics that are robust to outliers. In the case of robust scaling,...
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