During the training of the decision tree algorithm for a classification task, the dataset is split into subsets on the basis of features. After the training, the overall importance of a feature in a decision tree can be computed in the following way:1) Go through all the splits for which the feature was used and … Read more
SGDRegressor and SGDClassifier use stochastic gradient descent (SGD) as an optimization algorithm. That makes the model very efficient for large datasets because due to SGD, the model parameters are updated after each training sample instead of the entire dataset. Moreover, both algorithms have a lot of flexibility regarding different loss functions, methods to prevent overfitting, … Read more
