Bootstrapping allows us to

Related MCQs

• Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features
• Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features
• Like the probabilistic view, the ________ view allows us to associate a probability of membership with each classification.
• The parameter            allows specifying the percentage of elements to put into the test/training set
•            allows exploiting the natural sparsity of data while extracting principal components.
• there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called           
• The parameter______ allows specifying the percentage of elements to put into the test/training set
• there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called _____