Set 3

 What is Machine learning?

• The selective acquisition of knowledge through the use of computer programs

• The selective acquisition of knowledge through the use of manual programs

• The autonomous acquisition of knowledge through the use of computer programs

• The autonomous acquisition of knowledge through the use of manual programs

An artificially intelligent car decreases its speed based on its distance from the car in front of it. Which algorithm is used?

• Naïve-Bayes

• Decision Tree

• Linear Regression

• Logistic Regression

What is the Manhattan distance between a data point (9, 7) and a new query instance (3, 4)?

• 7

• 9

• 3

• 4

Assuming that we have a dataset with little noise, a model is underfitting when:

• Both the train and test errors are high

• Train error is low but test error is high

• Train error is high but the test error is low

• Both train and test errors are low

Which of the following evaluation metrics would you use to measure the performance of a classification model?

• Precision

• Recall

• F1 Score

• Mean Squared error

Stratification in machine learning is primarily used for

• Reducing the size of the training dataset.

• Dividing data for cross-validation.

• Addressing imbalanced class distributions.

• All of the above.

In K-Fold Cross Validation, the data is divided into

• Two sets: training and testing.

• K non-overlapping folds of equal size.

• A random selection of training and testing data.

• Stratified groups based on the target variable.

The value of K in KNN refers to:

• The number of features used in the model.

• The number of iterations required for training.

• The number of nearest neighbors used for prediction

• pre-defined threshold for distance calculations.

The curse of dimensionality refers to the challenges that arise in machine learning when

• The data has too few features

• The data is not properly normalized.

• The number of features (dimensions) increases significantly.

• The target variable is not continuous.

A positive covariance between two features indicates

• The features are completely independent of each other.

• As one feature increases, the other tends to decrease

• There is no linear relationship between the features.

• As one feature increases, the other tends to increase as well.