Modelling and Evaluation

What are the two main types of AI modelling techniques?

1. Neural Networks and Decision Trees
2. Supervised and Unsupervised Learning
3. Rule-Based and Learning-Based Models
4. Reinforcement Learning and Clustering

Answer

Rule-Based and Learning-Based Models

Reason — The two primary types of modelling techniques in AI are rule-based and learning-based models.

Which type of AI model follows specific rules set by humans?

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
4. Rule-Based Models

Answer

Rule-Based Models

Reason — Rule-based modelling is a way to make decisions using a list of specific rules. These rules are predefined and followed step by step, that is, they are set beforehand by humans.

What is a key disadvantage of rule-based modelling?

1. Flexibility
2. Ability to handle unexpected scenarios
3. Learning from new data
4. Consistent behaviour

Answer

Ability to handle unexpected scenarios

Reason — A key disadvantage of rule-based modelling is that the system can only handle situations covered by the predefined rules, and therefore unexpected scenarios can be challenging.

Which component of a decision tree represents the initial point where the first decision occurs?

1. Branch
2. Leaf Node
3. Decision Node
4. Root Node

Answer

Root Node

Reason — The root node represents the initial point of the decision tree where the first decision occurs.

Reinforcement learning can be compared to which human process?

1. Learning by instruction
2. Learning from mistakes
3. Following rules
4. Memorisation

Answer

Learning from mistakes

Reason — Reinforcement learning is also referred to as learning from mistakes, as the machine learns through the process of trial and error.

What is the F1 Score a harmonic mean of?

1. Accuracy and specificity
2. Precision and recall
3. Sensitivity and specificity
4. True positive and false positive rates

Answer

Precision and recall

Reason — F1 Score is the harmonic mean of precision and recall, providing a balance between the two metrics.

Fill in the blanks:

1. ................ learning models improve over time by learning from data, without following strict predefined rules.
2. Supervised learning requires a large amount of ................ data, which can be time-consuming and expensive to collect.
3. Reinforcement learning is often compared to the human learning process of ................ and error.
4. Evaluation metrics like accuracy and ................ score are used to assess the performance of an AI model.
5. In a decision tree, the terminal points where final outcomes are determined are called ................ .
6. ................ deployment approach processes data directly on devices like IoT sensors or smartphones.

Answer

1. Learning-based learning models improve over time by learning from data, without following strict predefined rules.
2. Supervised learning requires a large amount of labelled data, which can be time-consuming and expensive to collect.
3. Reinforcement learning is often compared to the human learning process of trial and error.
4. Evaluation metrics like accuracy and F1 score are used to assess the performance of an AI model.
5. In a decision tree, the terminal points where final outcomes are determined are called leaf nodes.
6. Edge deployment approach processes data directly on devices like IoT sensors or smartphones.

Assertion (A): Learning-based models can adapt to new data and improve over time.

Reason (R): Learning-based models are limited to predefined rules and cannot modify their behaviour without manual updates.

Based on the above assertion and reasoning, pick an appropriate statement from the options given below:

1. Both A and R are true and R is the correct explanation of A.
2. Both A and R are true and R is not the correct explanation of A.
3. A is true but R is false.
4. A is false but R is true.
5. Both A and R are false.

Answer

A is true but R is false.

Reason — Learning-based models learn from data and improve over time, eliminating the need for manual rule updates. However, being limited to predefined rules and not able to modify behaviour without manual updates is a characteristic of rule-based models, not learning-based models.

After learning the various evaluation techniques for a model, now it's time to deploy the model. Deployment of a model refers to making the model accessible for general usage and timely updates to achieve the desired result.

Let's now understand the different approaches to AI project deployment:

1. What does the deployment of an AI model refer to?
   1. Creating the AI model
   2. Making the model accessible for general usage and timely updates
   3. Evaluating the AI model
   4. Training the AI model
2. Why is the deployment of an AI model important?
   1. It helps in creating new data
   2. It ensures the model is used by the intended users and updated timely
   3. It helps in deleting old data
   4. It is used only for testing purposes
3. What does the deployment of the library assistant application enable?
   1. General usage by students, library staff and faculty members
   2. Only data collection
   3. Only model training
   4. Model evaluation
4. Which phase comes after learning various evaluation techniques for a model?
   1. Data collection
   2. Model training
   3. Model deployment
   4. Data visualisation

Answer

1. Making the model accessible for general usage and timely updates.
   Reason — Deployment of a model refers to making the model accessible for general usage and its timely updating to achieve the desired result.

2. It ensures the model is used by the intended users and updated timely.
   Reason — Deployment is important because the AI model becomes available for real-world use and can be updated to achieve the desired result.

3. General usage by students, library staff and faculty members.
   Reason — The library assistant application can only be useful if it is deployed so that it can be accessed by the students, library staff and faculty members.

4. Model deployment.
   Reason — After learning the various evaluation techniques for a model, the next phase is to deploy the model, making it accessible for general usage.

Write short notes on Decision Nodes.

Answer

Decision nodes are locations within a decision tree where decisions are required, resulting in further branches based on the outcome of the decision.

Write short notes on Model Prediction.

Answer

In the prediction phase, once the model is trained, new input data is given to the model and it predicts the output based on what it learned during training.

Write short notes on Leaf Nodes.

Answer

Leaf nodes are the terminal points of a decision tree where final decisions or outcomes are determined.

Write short notes on Edge Deployment.

Answer

In edge deployment, the AI model is deployed directly on devices like smartphones, sensors or IoT devices, where data is processed near its source to provide quick results with low latency and offline capability.

Write short notes on F1 Score.

Answer

F1 Score is the harmonic mean of precision and recall, providing a balance between the two metrics used to evaluate the performance of an AI model.

Write short notes on Regression.

Answer

Regression is a type of statistical and machine learning technique used to model the relationship between a dependent variable (target) and one or more independent variables (predictors). It is a form of supervised learning that uses labelled datasets to predict continuous outputs, such as house prices, sales growth or weather conditions.

Write short notes on True Positive.

Answer

True Positive (TP) symbolises that the model correctly predicted the output. For example, if an image contains a cat and the model correctly detects the cat, it is a true positive.

Write short notes on True Negative.

Answer

True Negative (TN) is when the model rightly identifies a negative case. For example, if a model correctly recognises that there is no cat in an image without a cat, it is a true negative.

Explain the role of evaluation in the AI project cycle.

Answer

Evaluation is one of the most crucial phases in the AI project cycle. It assesses how well an AI model performs its intended task and helps ensure that the model is reliable, accurate and effective in real-world applications. The primary objective of evaluation is to identify the model's strengths and weaknesses and decide whether it is ready for deployment or needs further improvement.

Explain the concept of the ROC-AUC in a model evaluation.

Answer

ROC-AUC is used to evaluate the performance of a classification model. The ROC (Receiver Operating Characteristic) curve is a graphical representation that shows the relationship between the True Positive Rate (TPR) on the y-axis and the False Positive Rate (FPR) on the x-axis at different threshold values. AUC (Area Under Curve) is a single scalar value that summarises the overall performance of the ROC curve and shows how well the model can distinguish between positive and negative classes.

Distinguish between cloud deployment and on-premises deployment of AI models.

Answer

Differences between cloud deployment and on-premises deployment of AI models are:

What are the advantages of Edge Deployment?

Answer

The advantages of Edge Deployment are as follows:

• Low Latency: It provides real-time results as data does not need to be sent to the cloud for processing.
• Offline Capability: It can function without an internet connection.

What are the advantages and disadvantages of rule-based modelling?

Answer

Advantages of Rule-Based Modelling:

1. It is easy to understand, as the system’s decisions are clear and follow straightforward rules.
2. It is simple to create, since setting up a rule-based system only requires defining rules.
3. It is consistent, as the system behaves predictably by always following the same rules under the same conditions.

Disadvantages of Rule-Based Modelling:

1. It is not flexible, as the system can only handle situations covered by predefined rules.
2. It is difficult to manage, because maintaining and updating the system becomes harder as the number of rules increases.
3. There is no learning, as the system does not learn from new experiences and uses the same rules unless manually updated.

What are the different phases of a learning-based AI model?

Answer

The different phases of a learning-based AI model are:

1. Training Phase
2. Learning Phase
3. Prediction Phase

Why is data important in the training phase of a learning-based model?

Answer

Data is important in the training phase because the learning-based model analyses the given data to learn patterns and features, which helps it build a model that can make accurate predictions. The quality and quantity of data directly affect how well the model learns and performs.

What are the key characteristics of supervised learning?

Answer

The key characteristics of supervised learning are as follows:

1. It learns from labelled data, where the correct output is already known.
2. The model is trained using input data and corresponding output provided by a supervisor.
3. It is used to analyse labelled datasets and discover hidden patterns to arrive at an output.
4. Once trained, the model can predict correct answers for new or similar input data.

Enlist two advantages and two disadvantages of supervised learning.

Answer

Advantages of Supervised Learning:

1. Supervised learning often provides very accurate results because the model is trained on labelled data, meaning it knows the correct answers during training.
2. Supervised learning is easier to predict how the model will perform because it learns from data that has clear and correct labels.

Disadvantages of Supervised Learning:

1. Supervised learning requires a large amount of labelled data, which can be time-consuming and expensive to collect.
2. Sometimes, the model might become too focused on the training data and not perform well on new, unseen data. This is called overfitting.

Name the various techniques used in the supervised learning process.

Answer

The various techniques used in the supervised learning process are:

1. Regression Techniques
2. Classification Techniques
3. Neural Network Techniques

Explain the real-world application of supervised learning with the help of an example.

Answer

One real-world application of supervised learning is email spam detection. In this application, supervised learning models classify emails as spam or not spam based on labelled examples of past emails. The model learns from emails that are already marked as spam or not spam and then uses this learning to predict whether new incoming emails are spam or legitimate.

Distinguish between Supervised Learning and Unsupervised Learning.

Answer

Differences between Supervised Learning and Unsupervised Learning:

Distinguish between Positive Linear Regression and Negative Linear Regression relationship.

Answer

Differences between Positive Linear Regression and Negative Linear Regression relationship are:

What is meant by linear regression?

Answer

Linear regression is a supervised machine learning algorithm that establishes a linear relationship between a dependent variable and one or more independent features by fitting a linear equation to the observed data.

What are the benefits of linear regression?

Answer

The benefits of using the techniques of linear regression are as follows:

1. It is easy to understand, implement and interpret.
2. It is computationally efficient and suitable for small to medium-sized datasets.
3. The coefficients provide clear insight into the relationship between input variables and the output.
4. It effectively models relationships where the dependent and independent variables have a linear association.
5. It is useful for both academic and practical applications like trend analysis, forecasting and predictive modelling.

Enlist any three real world applications of linear regression.

Answer

Any three real-world applications of linear regression are:

1. It can estimate the value of a property based on features like location, size and age of the house.
2. It is an efficient tool to analyse historical sales data and predict future sales trends.
3. It can predict healthcare expenses based on patient demographics and medical history.

What are the challenges associated with unsupervised learning? Explain.

Answer

The challenges associated with unsupervised learning are as follows:

1. Complexity: Interpreting the results of unsupervised learning can be difficult, as there are no clear labels or outcomes to guide the analysis.
2. Uncertain accuracy: Without labelled data, it is challenging to evaluate how accurate or effective the model is, making it harder to measure success.
3. Requires domain knowledge: Understanding and validating the patterns discovered by unsupervised learning often require expert knowledge in the specific field.
4. Risk of overfitting: Since the model tries to find patterns in all the data, it might sometimes identify patterns that are not meaningful, leading to overfitting.
5. Not always suitable: Unsupervised learning may not be the best approach for tasks where specific outcomes or predictions are needed.

How does reinforcement learning differ from supervised and unsupervised learning?

Answer

Reinforcement learning differs from supervised and unsupervised learning in the way the model learns. In supervised learning, the model learns from labelled data under guidance, while in unsupervised learning, the model discovers patterns in unlabelled data without supervision. In contrast, reinforcement learning learns through trial and error, where an agent interacts with the environment, learns from its mistakes, and improves its actions by maximising rewards over time.