Supervised Learning

1. Task of inferring a model from labelled training data is called

• Unsupervised learning

• Supervised learning

• Reinforcement learning

• None of these

2. What are the key challenges in successfully training machine learning model?

• Features available in the data sets

• Algorithms that are suitable for the task

• Hyper parameters Tuning

• Evaluation Metrics

3. Which of these metrics is not used to evaluate classification algorithm
   

• Recall

• Precision

• Predicted vs True Chart

• F1 Score

4. Underfitting destroys the accuracy of the machine learning model

• True

• False

5. Overfitting has positive impact to the machine learning model and generalise well

• True

• False

6. Stratification is performed when the dataset is not balanced

• True

• False

7. The most widely used metrics and tools to assess a classification model are

• Confusion matrix

• Cost-sensitive accuracy

• Area under the ROC curve

• All of the above

8. Which of the following is a good test dataset characteristic

• Large enough to yield meaningful results

• Is representative of the dataset as a whole

• Both A and B

• None of the above

9. Which one below is not model evaluation techniques

• Independent test data

• Leave-one-out

• Hold-in

• K-fold cross-validation

10. Cost Sensitive Learning is focusing on errors of the model

• True

• False