Some people are using the term        instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.

Related MCQs

• Some people are using the term ___ instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.
• In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea
• Machine learning techniques differ from statistical techniques in that machine learning methods
• Which of the following are correct statement(s) about stacking? 1. A machine learning model is trained on predictions of multiple machine learning models 2. A Logistic regression will definitely work better in the second stage as compared to other classification methods 3. First stage models are trained on full / partial feature space of training data
• Which of the following are correct statement(s) about stacking? A machine learning model is trained on predictions of multiple machine learning models A Logistic regression will definitely work better in the second stage as compared to other classification methods First stage models are trained on full / partial feature space of training data
• Machine learning techniques differ from statistical techniques in that machine learning methods
• 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
• True or False: Ensemble learning can only be applied to supervised learning methods.
•            is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.
•             is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.