Machine Learning Quiz for Class 8

What is the main characteristic of supervised learning?

• Guessing the correct output

• Training on unlabeled data

• Training on labeled data

• Not using any data for training

Give an example of a supervised learning algorithm.

• Logistic Regression

• Linear Regression

• Decision Tree

• Unsupervised Learning

What is the goal of unsupervised learning?

• To measure the accuracy of a model's predictions

• To predict future outcomes based on historical data

• To classify input data into predefined categories

• To find hidden patterns or intrinsic structures in the input data without labeled responses.

Name a popular unsupervised learning technique.

• Clustering

• Regression

• Decision Tree

• Supervised Learning

What is reinforcement learning?

• A type of learning that focuses on physical activities and exercises

• A type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve maximum cumulative reward.

• A type of learning that relies on trial and error to make decisions

• A type of learning that involves memorizing facts and information

How is reinforcement learning different from supervised learning?

• Reinforcement learning uses unsupervised data, while supervised learning uses labeled data

• Reinforcement learning is about learning from the environment through trial and error, while supervised learning is about learning from labeled data with guidance from a teacher.

• Reinforcement learning does not require guidance from a teacher, while supervised learning does

• Reinforcement learning does not involve trial and error, while supervised learning does

What is semi-supervised learning?

• A type of machine learning that uses a small amount of labeled data and a large amount of unlabeled data for training.

• A type of learning that does not use any data for training

• A type of learning that only uses labeled data for training

• A type of learning that uses a large amount of labeled data and a small amount of unlabeled data for training

When is semi-supervised learning used?

• When there is a small amount of labeled data and a large amount of unlabeled data.

• When there is a large amount of labeled data and a small amount of unlabeled data.

• When the data is evenly distributed between labeled and unlabeled.

• When there is no labeled data available.

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All three of these AI concepts – machine learning, deep learning, and neural networks – can enable hardware and software robots to “think” and act dynamically, outside the confines of _______________.

• Code

• Human Brain

What are the advantages of semi-supervised learning?

• More cost-effective and efficient learning from both labeled and unlabeled data

• Higher cost and less efficient

• Limited to only labeled data

• Less accurate predictions