AI & ML Chapter-2 Part-A

What distinguishes machine learning from artificial intelligence?

• Machine learning focuses on simulating human behavior,while AI focuses on learning from data.
• AI aims to create intelligent machines, while machine learning specifically allows machines to learn from data.
• Machine learning is a broader concept than AI.
• AI is solely concerned with maximizing accuracy and patterns.

Which subset of AI is primarily concerned with accuracy and patterns?

• Machine learning
• Deep learning
• Supervised learning
• Unsupervised learning

Which type of machine learning is concerned with making accurate predictions based on past data?

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Weak AI

What is the scope of machine learning compared to artificial intelligence?

• Machine learning has a broader scope than AI.
• Both AI and machine learning have similar scopes.
• AI has a wider range of applications than machine learning.
• AI is limited in scope compared to machine learning.

What is one primary reason for employing Machine Learning in problem-solving tasks?

• Efficiency
• Complexity
• Infeasibility
• Simplicity

Which aspect of traditional programming becomes impractical in complex scenarios, prompting the need for Machine Learning?

• Efficiency
• Complexity
• Infeasibility
• Simplicity

In traditional programming, who formulates or codes the rules for processing data?

• Machine Learning algorithms
• End-users
• Programmers
• Data analysts

What is a key advantage of Machine Learning over Traditional Programming in terms of rule formulation?

• Complexity reduction
• Flexibility enhancement
• Automatic rule formulation
• Improved program performance

What is the typical proportion of data used for training in the train-test set split method?

• 50% for training and 50% for testing
• 70-80% for training and 20-30% for testing
• 90% for training and 10% for testing
• 95% for training and 5% for testing

What is the purpose of the test dataset in the train-test split evaluation procedure?

• To train the machine learning model
• To assess the model's performance on unseen data
• To fine-tune hyperparameters
• To gather more data