Modelling | CBSE Class 9 AI
1. What do you mean by modelling ?
It is the process of developing, training and testing an AI Model.
2. What activities are performed during modelling phase?
- AI models are developed.
- Best model among them is chosen.
- After choosing the best model, algorithms are written.
- Training of AI model is performed.
- Testing of AI model is done.
3. What is the significance of modelling ?
It is used to identify the problem and training of AI model. It is also used for optimization of the AI model.
4. What do you mean by a rule based model?
It is an AI model that can solve problems by using predefined rules and patterns.
5. Why is rule based models called static model?
Rule based models are called static because they do not change with time.
6. What is learning based model?
It is an AI model that can learn from a large amount of data. There are not predefined rules. Machine learns with new data.
7. Why are learning based system called dynamic?
Learning based models are called dynamic because they continuously learn and improve their performance with new data.
8. Define Machine Learning.
It is the ability of machines to learn and improve tasks with experience and by the use of data.
9. Define deep learning.
Deep learning is a subset of machine learning in which machine learns by filtering the input data . It can take complex decision by using neural networks.
10. What are the different types of learning based models?
i. Supervised learning
ii. Semi supervised learning
iii. Unsupervised learning
iv. Reinforcement learning
11. What is labelled and unlabeled data?
i. Data without any explanatory tags or names is called unlabeled data.
ii. Data with explanatory tags and names is called labelled data.
12.What do you mean by supervised Learning?
It is a machine learning approach in which a machine learns from a labelled data.
13. What you mean by supervised learning models?
In supervised learning, an AI model is trained using labeled data — which means the data already has correct answers. The model learns from this data to predict the right output for new inputs.
14. Which type of models have a set of expected outputs along with the input data?
Supervised learning
15. Write advantages of supervised learning.
i. It is easy to understand.
ii. It gives very accurate results.
iii. It works well when the user has experience with similar problems.
16. Write disadvantages of supervised learning.
i. It uses labeled data, so the data needs to be cleaned and prepared first.
ii. It takes a lot of time to process.
iii. The training data should come from real-life examples.
17. Name two supervised learning techniques?
i. Regression
ii. Classification.
18.What do you mean by regression?
This is a supervised learning method that finds the relationship between different variables and gives an output.
19.What do you mean by classification?
This is a supervised learning method that classifies new data based on what it learned from labeled training data.
20. Give some examples where classification is useful.
Document classification, Malware classification, fraud detection and detecting spam mails.
21. Give some examples where Regression is useful.
House Prices forecasting, stock prices and weather forecasting.
22. What do you mean by unsupervised learning model?
It is a type of machine learning that learns from data without human supervision.
23 How does an unsupervised learning models work?
Unsupervised learning uses algorithms to find patterns and group unlabeled data without any human help.
24 Which types of models do not get expected outputs along with the input data?
Unsupervised learning
25. Write advantages of unsupervised learning.
i. It helps to find hidden patterns in data.
ii. It is useful in real situations because it works with unlabeled data.
iii. It learns and improves in real time.
26. Write disadvantages of unsupervised learning.
i. The results may not be very accurate.
ii. It may not give the expected output because it uses unlabeled data.
27. Write names of Unsupervised learning techniques.
i. Clustering
ii. Association
iii. Dimensionality Reduction
28. What do mean by clustering? Gives examples.
It is an unsupervised learning method that groups unlabeled data based on how similar or different they are.
Examples: It can be used to detect fake news or group similar documents by their features.
29. What is Association ? Gives examples.
Association is an unsupervised learning method that finds useful relationships between features in a dataset.
Example: It is used on shopping websites to recommend products based on customers’ buying habits.
30. What is Dimensionally reduction? Gives examples.
Dimensionality reduction is a method to reduce the number of input variables while keeping the meaning of the data the same. It helps make problems simpler.
Example: It is used in data visualization, video compression, and studying human genes.
31. What is Semi Supervised learning?
Semi-supervised learning combines supervised and unsupervised methods. It trains the AI model using both labeled and unlabeled data.
32. What is reinforcement learning?
It is a technique where the machine learns through rewards and penalties. The learning system, called an agent, interacts with its environment, performs actions, and gets rewards for correct outputs and penalties for wrong ones.
33 Explain the components of reinforcement learning model?
i. Agent: The system or program that learns.
ii. Environment: The surroundings or things the agent interacts with.
iii. Policy: The set of rules the agent follows to act.
iv. Action: The steps or moves made by the agent.
v. Reward: The feedback the agent gets for its actions.
34. Explain different types of reinforcement learning models?
There are two types of reinforcement learning models:
i. Positive Reinforcement: It increases rewards to encourage a certain behavior.
ii. Negative Reinforcement: It removes penalties to encourage a certain behavior.
35. Give some examples of applications of reinforcement learning models.
i. Teaching self-driving cars to drive safely.
ii. Training robots to handle risky tasks.
iii. Building recommendation systems that learn from experience.
36. Write advantages of reinforcement learning.
i. It looks at the entire problem, not just parts of it.
ii. It can work well in new or unknown situations.
iii. It helps solve very complex problems.
iv. It can fix mistakes while learning.
v. It prevents the same errors from happening again.
37. Write disadvantages of reinforcement learning.
i. It is not good for simple problems.
ii. It needs a large amount of data.
iii. It can take a lot of time.
iv. It is costly to use.
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