While using           all labels are turned into sequential numbers.

Related MCQs

• While using _____ all labels are turned into sequential numbers.
• In which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0.
• In many classification problems, the target             is made up of categorical labels which cannot immediately be processed by any algorithm.
• In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least         valid options
• In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least          valid options
• In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least valid options
• In many classification problems, the target ______ is made up of categorical labels which cannot immediately be processed by any algorithm.
• In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least_____valid options
• Suppose you are using stacking with n different machine learning algorithms with k folds on data. Which of the following is true about one level (m base models + 1 stacker) stacking? Note: Here, we are working on binary classification problem All base models are trained on all features You are using k folds for base models
•              produce sparse matrices of real numbers that can be fed into any machine learning model.