If I am using all features of my dataset and I achieve 100% accura

Related MCQs

• If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?
• If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?
• If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?
•            dataset with many features contains information proportional to the independence of all features and their variance.
•            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.
• ______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.
• Which of the following methods can not achieve zero training error on any linearly separable dataset?
• Consider the following dataset. x,y,z are the features and T is a class(1/0). Classify the test data (0,0,1) as values of x,y,z respectively.
• 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