UNIT 1 - ML

1) Feedback-based learning method is called as _______

• a) Supervised

• b) Unsupervised

• c) Reinforcement

• d) Self learning

2) Yes/No – This is the example for which type of data.

• a) Continuous

• b) Categorical

• c) Numerical

• d) Ordinal data

3) Low bias and high variance leads to____

• a) Biased

• b) Underfitting

• c) Perfect

• d) Overfitting

4)Which is the technique to avoid overfitting?

• a) Accuracy

• b) Cross Validation

• c) Confusion matrix

• d) Training

5) When our machine learning model is not able to capture the underlying trend of the data then it is_____

• a) Underfitting

• b) Testing model

• c) Training model

• d) Overfitting

6) Bias and Variance is ______ errors.

• a)  Undefined Error

• b) Irreducible Error

• a) Reducible Error

• d) Biased error

7) Which of the following is the evaluation metrics for Regression problems_______

• a) Mean Absolute error

• b) Accuracy

• c) Condusion matrix

• d) Precision

8) Which of the following is the example for Classification problems_______.

• a) Mean Squared error

• b) R2 Score

• c) Adjusted R

• d) Recall

9) Accuracy is good to use when the _______

• c) Independent variable classes in data are imbalanced

• b) Target variable classes in data are imbalanced

• c) Target variable classes in data are approximately balanced

• c) Independent variables are same as dependent variables

10) Ratio between the numbers of Positive samples correctly classified as Positive to the total number of Positive samples is called_____.

• a) Recall

• b) R2 Score

• c) Accuracy

• d) Precision