Class 10 AI Chapter - Evaluating Models Topic - Question/Answer

 

Class 10 Artificial Intelligent Code 417 Solutions

Session 2025-26

Artificial Intelligence code 417 syllabus pdf class 10 solutions. Class 10 AI Book. Part - A,  Part - B, and Python. This article provides complete solution for class 10 AI (Artificial Intelligence)  Code - 417 solution according to new syllabus 2025 – 26. In this article first of all go through the AI Code - 417 syllabus and then follow chapter wise notes. Class 10 AI MCQs Quiz with answers.

Part A - Employability Skills Question/Answers 

Chapter 1 -Communication Skills - II 

Chapter 2 -Self - Management Skills - II    

Chapter 3 - ICT Skills - II 

Chapter 4 - Entrepreneurial Skills - II 

Chapter 5 - Green Skills - II

Part B - Subject Specific Skills Question/Answers 

Chapter 1 -AI Project Cycle & Ethics 

Chapter 2 - Modelling in AI

Chapter 3 - Evaluating Models

• Question Answer - 1
• Question Answer - 2
• Question Answer - 3
• Question Answer - 4

Chapter 4 - Statistical Data

Chapter 5 - Computer Vision

Chapter 6 - Natural Language Processing

Chapter 7 - Advance Python

#What is evaluation in AI project cycle?

Evaluation is the fifth stage of the AI Project Cycle, where the performance of the trained AI model is tested and measured to check how accurately it can make predictions or decisions on new, unseen data.

It helps us determine whether the model meets the project goals and can be used in real-world situations.

In Simple Words:

Evaluation means checking how good or bad an AI model is after training — just like testing a student’s knowledge after studying for an exam.

Importance of Evaluation

1. Checks Model Accuracy:
   Evaluation helps to find out how accurately the model can predict correct results.

2. Ensures Reliability:
   It ensures the model gives consistent and dependable outputs every time.

3. Measures Real-World Performance:
   Evaluation shows how the model behaves with real data, not just the data it learned from.

4. Helps Improve the Model:
   If the model performs poorly, evaluation results help in identifying errors and improving the model through retraining or tuning.

5. Supports Decision-Making:
   It helps developers and organizations decide whether the model is ready for deployment or needs more work.
   
   

Need for Evaluation

1. To Verify Learning: Evaluation checks whether the model has genuinely learned patterns from the data or has simply memorized the examples.
   
   
2. To Detect Bias or Errors: It helps identify if the model gives unfair, biased, or incorrect results, ensuring ethical and accurate predictions. 
   
   
3. To Compare Models: When multiple models are built for the same problem, evaluation helps select the one that performs best. 
   
   
4. To Ensure Quality and Accuracy: Evaluation ensures that the model meets the desired accuracy standards and performance goals before it is used practically. 
   
   
5. To Gain Trust and Confidence: A well-evaluated model builds confidence among users and developers, showing that it can be trusted in real-world use.

Example:

Suppose an AI model predicts whether a patient has diabetes or not.

• If the model correctly predicts 95 out of 100 cases, Accuracy = 95%, showing that it performs well. But if it often gives wrong results, we’ll know it needs improvement through evaluation.

	#What is Training-Test Split of Dataset for Evaluation? In an AI project, the training-test split means dividing the dataset into two parts — one for training the model and the other for testing it. Training Set → Used to teach the AI model patterns, features, and relationships in the data. Testing Set → Used to check how accurately the trained model performs on new, unseen data. In Simple Words: It’s like studying some chapters (training) and then taking an exam from unseen questions (testing) to check what you’ve actually learned.   Typical Split Ratio: Usually, the dataset is divided as: 80% for training 20% for testing (or sometimes 70%-30%, depending on the project). Example: Suppose we have 1,000 records of students with their study hours and exam results. 800 records (80%) → used to train the model. 200 records (20%) → used to test the model. If the model correctly predicts 180 out of 200 test cases, ✅ Accuracy = 90% Need for Training-Test Split To Measure Real Performance: The testing data helps us check how well the model performs on completely new data. This shows the model’s real-world capability. To Avoid Overfitting: If the model is only tested on the same data it was trained on, it might just memorize it. Splitting the data ensures the model learns general patterns instead of memorizing. To Ensure Fair Evaluation: Using separate testing data makes the evaluation unbiased. The performance results will be fair and realistic. To Compare Different Models: When multiple models are trained, the same testing data can be used to compare their performance and select the best one. To Build Confidence in the Model: If the model performs well on unseen test data, it gives confidence that the model can handle real-world scenarios effectively.
	#What are the Evaluation Models in machine learning? An evaluation technique in Artificial Intelligence (AI) or Machine Learning (ML) is a method used to measure the performance and accuracy of an AI model. It involves a machine learning model's performance on training and test data. It helps determine how well the model has learned from data and how effectively it can make predictions on unseen data. In simple terms, evaluation techniques are tests that check whether an AI model is good, average, or poor at solving a given problem. Purpose of Evaluation Techniques: To test model accuracy and reliability. To detect errors or weaknesses in predictions. To compare different models and select the best one. To ensure the model works well with real-world data. Evaluation Models Model Fitting in Evaluation When we train an AI model, it tries to learn patterns from data. Depending on how well it learns, we get three main types of fits: 1. Overfitting Meaning: The model learns the training data too well, including noise and unnecessary details. Result: Performs very well on training data but poorly on new (testing) data. Cause: The model becomes too complex (too many parameters or layers). Example: A student memorizes answers instead of understanding concepts — gets high marks on known questions but fails in new ones. Characteristics: High training accuracy Low testing accuracy Poor generalization 2. Underfitting  Meaning: The model fails to learn from the training data properly. Result: Performs poorly on both training and testing data. Cause: The model is too simple to capture the data’s patterns. Example: A student studies very little, so cannot answer any type of question properly. Characteristics: Low training accuracy Low testing accuracy Misses important data relationships 3. Perfect Fit (or Good Fit)  Meaning: The model learns the data patterns correctly — not too little, not too much. Result: Performs well on both training and testing data. Cause: Proper balance between model complexity and data size. Example: A student understands the concepts and performs well on both practice and exam questions. Characteristics: High training accuracy High testing accuracy Good generalization to new data #What is Classification? Classification is a type of supervised learning in Artificial Intelligence (AI) and Machine Learning (ML) where the goal is to categorize data into predefined classes or groups. In simple terms, classification means predicting a label or category for given input data. Examples of Classification: Predicting whether an email is spam or not spam  Checking whether a patient has diabetes or not  Identifying if a picture is of a cat or a dog How it Works: The model is trained on labeled data (data with known outcomes). It learns patterns that distinguish one class from another. When new data is given, the model predicts the correct class. Classification Metrics Once a classification model is built, we need to evaluate its performance — how well it predicts correct classes. This is done using several classification metrics. Confusion Matrix A confusion matrix is a table that shows how well the classification model performed by comparing actual vs predicted results. Actual / Predicted Predicted Positive Predicted Negative Actual Positive True Positive (TP) – Model correctly predicted positive False Negative (FN) – Model missed positive case Actual Negative False Positive (FP) – Model predicted positive incorrectly True Negative (TN) – Model correctly predicted negative 1. Classification Accuracy Definition: It measures the overall correctness of the model — the percentage of correct predictions. $$\text{Accuracy} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{FP} + \text{FN} + \text{TN}}$$ Meaning: Out of all predictions, how many were correct. Example: If out of 100 predictions, 90 are correct → Accuracy = 90% 2. Precision Definition: Precision measures how many of the predicted positives were actually positive. $$\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}}$$ Meaning: When the model says “Yes,” how often is it right? Example: If the model predicted 50 positive cases, but only 45 were correct. Precision = 45 / 50 = 0.9 (90%) Use: Important when false positives are costly (e.g., spam email detection). 3. Recall  Definition: Recall measures how many of the actual positives were correctly identified by the model. $$\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}}$$ Meaning: Out of all actual positive cases, how many did the model find? Example: If there were 60 actual positive cases, and the model found 54. Recall = 54 / 60 = 0.9 (90%) Use: Important when missing a positive case is serious (e.g., medical diagnosis). 5. F1 Score Definition: The harmonic mean of precision and recall. It balances both. $$\text{F1 Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}$$ Real-life example: If your spam filter’s precision is 83.3% and recall is 87.5%, $$\text{F1} \approx 85.3\%$$ High F1 means the model is good at both catching positives and avoiding false alarms.     Part A - Employability Skills Notes  Chapter 1 - Communication Skills - II Chapter 2 - Self Management Skills - II  Chapter 3 - ICT Skill - II Chapter 4 - Entrepreneurial Skills - II Chapter 5 - Green Skills - II Part B - Subject Specific Skills Notes  Chapter 1 -AI Project Cycle & Ethics Chapter 2 - Modelling in AI Chapter 3 - Evaluating Models Evaluation Techniques Confusion Matrix Chapter 4 - Statistical Data Chapter 5 - Computer Vision Chapter 6 - Natural Language Processing Chapter 7 - Advance Python Part A - Employability Skills MCQs  Chapter 1 - Communication Skills - II Chapter 2 - Self - Management Skills - II Chapter 3 - ICT Skill - II Chapter 4 - Entrepreneurial Skills - II  Chapter 5 - Green Skills - II Part B - Subject Specific Skills MCQs  Chapter 1 -AI Project Cycle & Ethics Chapter 2 - Modelling in AI   Chapter 3 - Evaluating Models Chapter 4 - Statistical Data Chapter 5 - Computer Vision Chapter 6 - Natural Language Processing Chapter 7 - Advance Python   Class 10 Resource Corner --------------------------------------------------         Class 10 AI Sample Paper 2022-23   Class 10 English  --------------------------------------------------