Advance Concepts of Modeling in AI Class 10 Questions and Answers – The CBSE has changed the syllabus of Std. X. The Questions and Answers are made based on the new syllabus and based on the CBSE textbook, Sample paper and Board Paper. All the important Information are taken from the Artificial Intelligence Class X Textbook Based on CBSE Board Pattern.
Advance Concepts of Modeling in AI Class 10 Questions and Answers
Q1. Give difference between rule based and learning based AI models.
Answer: The difference between rule based and learning based AI models are –
| Supervised Learning | Unsupervised Learning |
|---|---|
| Deals with labelled data | Deals with unlabeled data |
| Useful in real-world problems like predicting the prices of an item something based on past trends. | Useful in finding unknown patterns within data like making sence of a large number of observations from an experimental device. |
| Computing power required is simpler as clean labelled data is used as input. | The computing power required is more complex as unsorted and messy data is used as input |
Q2. What is supervised, unsupervised and reinforcement learning? Explain with examples.
Answer:
Supervised Learning – Supervised learning is a machine learning technique that uses labeled data to train algorithms to predict outcomes. Example of supervised learning is Email spam filtering and Image recognition
Unsupervised Learning – An unsupervised learning model works on unlabeled dataset. This means that the data which is fed to the machine is random and there is a possibility that the person who is training the model does not have any information regarding it. It helps the user in understanding what the data is about and what are the major features identified by the machine in it. Example of unsupervised learning is medical image and data exploration.
Reinforcement Learning – This learning approach enables the computer to make a series of decisions that maximize a reward metric for the task without human intervention and without being explicitly programmed to achieve the task. It’s based on a trial-and-error learning process to achieve the goals. Examples of reinforcement learning are question and answering, machine translation, and text summarization.
Q3. What is clustering and how is it different from classification?
Answer: Classification uses predefined classes in which objects are assigned. Clustering finds similarities between objects and places them in the same cluster and it differentiates them from objects in other clusters.
Q4. Explain neural networks. Also give functions of three layers of neural networks.
Answer: Neural networks are modelled on the human brain and nervous system. They are able to automatically extract features without input from the programmer.
These neurons are known as nodes. An artificial neural network has an input layer, an output layer, and hidden layers. The input layer is responsible for receiving the data from the real world, and all the input data passes through one or multiple hidden layers and transforms the result using the output layer.
Q5. Differentiate between classification and regression model.
Answer: the data is classified according to the labels. For example, in the grading system, students are classified on the basis of the grades they obtain with respect to their marks in the examination. In regression algorithms predict a continuous value based on the input variables. Continuous values as Temperature, Price, Income, Age, etc
Q6. Identify the type of learning (supervised, unsupervised, reinforcement learning) are the following case studies most likely based on?
a) Case Study 1: A company wants to predict customer churn based on past purchasing behavior, demographics, and customer interactions. They have a dataset with labeled examples of customers who churned and those who did not.
Answer: Supervised Learning
b) Case Study 2: A social media platform wants to group users based on their interests and behavior to recommend relevant content. They have a large dataset of user interactions but no predefined categories. Which type of learning is this case study most likely based on?
Answer: Unsupervised Learning
c) Case Study 3: An autonomous vehicle is learning to navigate through a city environment. It receives feedback in the form of rewards for reaching its destination safely and penalties for traffic violations. Which type of learning is this case study most likely based on?
Answer: Reinforcement Learning
d) Case Study 4: A healthcare provider wants to identify patterns in patient data to personalize treatment plans. They have a dataset with various patient attributes but no predefined labels indicating specific treatment plans. Which type of learning is this case study most likely based on?
Answer: Unsupervised Learning
e) Case Study 5: A manufacturing company wants to optimize its production process by detecting anomalies in sensor data from machinery. They have a dataset with examples of normal and anomalous behavior. Which type of learning is this case study most likely based on?
Answer: Supervised Learning
Q7. Identify the type of model (classification, regression, clustering, association model) are the following case studies most likely based on?
a) A bank wants to predict whether a loan applicant will “default” or “non-default” on their loan payments. They have a dataset containing information such as income, credit score, loan amount, and employment status.
Answer: Classification
b) A real estate agency wants to predict the selling price of houses based on various features such as size, location, number of bedrooms, and bathrooms. They have a dataset containing historical sales data.
Answer: Regression
c) A marketing company wants to segment its customer base into distinct groups based on purchasing behavior for targeted marketing campaigns. They have a dataset containing information such as purchase history, frequency of purchases, and amount spent.
Answer: Clustering
d) A grocery store wants to identify associations between different products purchased by customers to understand which products are commonly bought together. They have a transaction dataset containing records of items purchased together during each transaction.
Answer: Association Model
Q8. A healthcare provider wants to improve patient care by predicting the length of hospital stays for different medical conditions. They have a dataset containing patient demographics, medical history, and treatment details. The task involves:
a) To predict whether a patient will have a short or long hospital stay.
Answer: Classification
b) To predict the number of days a patient will stay in the hospital.
Answer: Regression
c) To segment patients into groups with similar characteristics for personalized treatment plans.
Answer: Clustering
d) To identify patterns in patient treatments and outcomes.
Answer: Association Model
Identify the type of model (classification, regression, clustering, and association model) in the above tasks.
Q9. Convert the following scenarios to perceptron:
a) Context: A manager is deciding whether to approve a work-from-home request from an employee.
Factors: – Does the employee perform well when working remotely? – Are there any upcoming team meetings or collaborative projects? – Does the company’s policy support remote work? – Is it beneficial for both the employee and the company?
Answer:
- Inputs Factors: Employee remote performance, upcoming team meetings/projects, company policy on remote work, benefits for employee and company.
- Weights: Importance of each factor.
- Threshold: Decision boundary for approval.
- Output: Approve (1) or Deny (0).
b) Context: A homeowner is deciding whether to invest in solar panels for their house.
Factors: – Do I have a sufficient average amount of sunlight in my area? – Are there any available incentives or rebates for installing solar panels? – Does installing solar panels impact the value of my home? – Does solar energy lead to environmental benefits?
Answer:
- Inputs (Factors): Average sunlight, available incentives/rebates, impact on home value, environmental benefits.
- Weights: Importance of each factor.
- Threshold: Decision boundary for investment.
- Output: Invest (1) or Not Invest (0).
Q10. Sirisha and Divisha want to make a model which will organize the unlabeled input data into groups based on features. Which learning model should they use and why?
Answer: Clustering model/Unsupervised learning is used to organize the unlabeled input data into groups
based on features. Clustering is an unsupervised learning algorithm which can cluster unknown data according to
the patterns or trends identified out of it. The patterns observed might be the ones which are known to the developer or it might even come up with some unique patterns out of it.
Q11. Identify and explain the types of the learning-based approaches in the figures given below.
Answer: The learning-based approaches shown in the given figures are Supervised learning and Unsupervised learning.
Figure 1: In a supervised learning model, the dataset which is fed to the machine is labelled. In other words, we can say that the dataset is known to the person who is training the machine only then he/she is able to label the
data. A label is some information which can be used as a tag for data. Here, labelled images of dog and cat are fed into the model and trained. The model correctly identifies the given input as dog.
Figure 2: An unsupervised learning model works on unlabelled dataset. This means that the data which is fed to the machine is random and there is a possibility that the person who is training the model does not have any information regarding it. The unsupervised learning models are used to identify relationships, patterns and
trends out of the data which is fed into it. It helps the user in understanding what the data is about and what are the major features identified by the machine in it. Here, images of a set of animals are fed into the AI model and the model clusters them based on similar features
Q12. Neural networks are said to be modelled the way how neurons in the human brain behave. A similar system is mimicked by the AI machine to perform certain tasks. Explain how neural networks work in an AI model and mention any three features of Neural Networks.
Answer: Neural networks are loosely modelled after how neurons in the human brain behave. The features of a neural network are :
- It is a fast and efficient way to solve problems for which the dataset is very large, such as in images.
- They are able to extract data features automatically without needing the input of the programmer.
- A neural network is essentially a system of organizing machine learning algorithms to perform certain tasks.
Q13. Why should we avoid using the training data for evaluation?
Answer: This is because our model will simply remember the whole training set, and will therefore always predict the correct label for any point in the training set.
14. Differentiate between Classification and Regression.
Answer:
| Classification | Regression |
|---|---|
| This model works on a discrete dataset which means the data need not be continuous. | Such models work on continuous data. |
| For example, in the grading system, students are classified on the basis of the grades they obtain with respect to their marks in the examination. | For example, if you wish to predict your next salary, then you would put in the data of your previous salary, any increments, etc and would train the model. |
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