PrelimExam - AppDev - FCPC

Supervised learning is best described as:

• Learning with no labeled data

• Learning from labeled examples to predict outputs for new inputs

• Learning to cluster similar items without labels

• Learning by random guessing

Which of the following is a typical supervised learning task?

• Clustering

• Dimensionality reduction

• Classification

• Anomaly detection (unsupervised)

In supervised learning terminology, the input variables are called:

• Targets

• Labels

• Features

• Losses

In supervised learning terminology, the output variable we predict is called:

• Features

• Labels (or targets)

• Hyperparameters

• Pipelines

Which algorithm family does Naive Bayes belong to?

• Instance-based learning

• Probabilistic classifiers

• Decision trees

• Neural networks

The "naive" assumption in Naive Bayes refers to:

• Features are sorted

• Features are independent given the class label

• Labels are independent of features

• The algorithm uses no probabilities

Which Naive Bayes variant is typically used for continuous (real-valued) features?

• MultinomialNB

• BernoulliNB

• GaussianNB

• CategoricalNB

Which Naive Bayes variant is commonly used for text classification with word counts?

• GaussianNB

• MultinomialNB

• BernoulliNB

• KNeighborsNB

BernoulliNB is particularly suitable when features are:

• Continuous numeric counts

• Binary (presence/absence) indicators

• Multi-class labels

• Time series

In scikit-learn, which method trains a classifier?

• transform()

• fit()

• predict_proba()

• score()