Q1: What is AUC (Area under the ROC Curve)?
- A: A number between 0.0 and 1.0 representing a binary classification model's ability to separate positive classes from negative classes.
- B: A metric used to evaluate the performance of a classification model by calculating the area under the ROC curve.
- C: A measure of the model's ability to correctly classify both positive and negative instances across different probability thresholds.
- D: All of the above
Answer: A
Q2: Bias in ethics/fairness can be defined as:
- A: Stereotyping, prejudice or favoritism towards some things, people, or groups over others.
- B: Systematic error introduced by a sampling or reporting procedure.
- C: Both A and B
- D: Neither A nor B
Answer: C
Q3: What is binary classification?
- A: A type of classification task that outputs a continuous value.
- B: A type of classification task that predicts one of two mutually exclusive classes.
- C: A type of classification task that predicts one of multiple classes.
- D: A type of regression task that predicts a continuous value within a specific range
Answer: B
Q4: What does convergence refer to in machine learning?
- A: A state reached when the model's performance on the training data plateaus.
- B: A state reached when the model's performance on the validation data plateaus.
- C: A state reached when the model's performance on the test data plateaus.
- D: A state reached when loss values change very little or not at all with each iteration
Answer: D
Q5: What is an epoch in machine learning?
- A: A full training pass over the entire dataset such that each example has been processed once.
- B: A single update of a model's weights during training.
- C: A small, randomly selected subset of the training data used in one iteration.
- D: The number of times the model's weights are updated during training on the entire dataset
Answer: A
Q6: What is a feature in machine learning?
- A: An input variable used in unsupervised learning.
- B: An output variable predicted by the model.
- C: An input variable used in supervised learning.
- D: A variable that is not used in the model
Answer: C
Q7: What is a regression model?
- A: A model that predicts a categorical output.
- B: A model that predicts a numerical output.
- C: A model that predicts a probability.
- D: A model that groups similar data points together
Answer: B
Q8: What is gradient descent?
- A: A technique to minimize loss by iteratively adjusting model parameters.
- B: A technique to maximize model performance by exploring different hyperparameter values.
- C: A technique to evaluate the performance of a model on unseen data.
- D: A technique to prevent overfitting by adding a penalty term to the loss function
Answer: A
Q9: What is inference in machine learning?
- A: The process of making predictions by applying a trained model to labeled examples.
- B: The process of training a model on a dataset.
- C: The process of evaluating a model's performance.
- D: The process of making predictions by applying a trained model to unlabeled examples
Answer: D
Q10: What is loss in machine learning?
- A: A measure of how well the model fits the training data.
- B: A measure of how well the model generalizes to unseen data.
- C: A measure of how far a model's prediction is from its label.
- D: A measure of the model's complexity
Answer: C