Grade 11- ML

Which of the following best describes a regression model in supervised learning?

• • A model that identifies clusters in data without predefined labels

• • A model that predicts a numeric value based on input data

• • A model that classifies data into one of two categories

• • A model that creates new content based on user input

1. Which ML category involves a model that predicts whether or not an email is spam?

• • Regression

• • Generative AI

• • Binary classification

• •  Clustering

1. In reinforcement learning, what is the purpose of a policy?

• • To identify patterns in unlabeled data

• • To summarize large datasets efficiently

• • To define the best strategy for maximizing rewards

• To classify input data into predefined categories

1. Which of the following is an example of generative AI in action?

• •  Predicting rainfall based on current weather data

• • Clustering sales data into customer segments

• • Producing a photorealistic image from textual descriptions

• • Recommending songs based on listening history

Which of the following scenarios is best suited for a regression model rather than a classification model?

• Determining whether a customer will buy a product (Yes/No).

• Predicting the likelihood that an email is spam or not spam.

• Estimating the price of a house based on location and features.

• Classifying an image as either a cat or a dog.

Which of the following best describes how an ML model is evaluated after training?

• The model is tested on the same dataset it was trained on to check accuracy.

• The model is tested on a labeled dataset, and its predictions are compared to true labels.

• The model is deployed and adjusted in real-time based on user feedback.

• The model is fine-tuned by adding new features without re-evaluating its performance.

1. What distinguishes supervised learning from unsupervised learning?

• • Supervised learning uses labeled data, while unsupervised learning does not.

• • Supervised learning identifies natural groupings in data, while unsupervised learning predicts numeric values.

• • Supervised learning relies on rewards and penalties, while unsupervised learning generates content.

• • Supervised learning does not require human intervention, while unsupervised learning does.

In Generative AI, what is the role of unsupervised learning during the initial training phase?

• To generate fully labeled datasets for supervised learning.

• To teach the model to mimic the patterns in data without explicit labels.

• To ensure the model does not overfit on specific examples.

• To perform feature selection for reducing model complexity.

1. What input data would a regression model predicting future ride times most likely use?

• • Current weather data and historical rainfall patterns

• • Traffic conditions, distance, and weather conditions

• • Labeled examples of photos containing traffic signs

• Clusters of similar ride times based on user feedback

In reinforcement learning, what is the primary objective of an agent?

• To minimize the number of input features.

• To classify unlabeled data into meaningful clusters.

• To find a policy that maximizes cumulative rewards over time.