AKTU Artificial Intelligence For Engineering(KMC 101) Quiz Part 4

Q:1. In Supervised Learning:

1.Input data is called training data and has a known label.

2.It can solve the classification and regression problems.

3.The training process continues until model achieves desired accuracy

4.All the above statements are true.

Solution- 4.All the above statements are true.

Reason 1- All the three 1,2, and 3 statements are true. As in supervised learning input data is called training data and has a known label. This is also used to solve the classification and regression problems. This training process continues until model achieves desired accuracy.

Reason 2- All the three 1,2, and three statements are true. As in supervised learning input file is named training data and features a known label. this is often also wont to solve the classification and regression problems. This training process continues until model achieves desired accuracy.

Q:2. In Unsupervised Learning, the incorrect statements are:

1.It organize data by similarity.

2.Input data know about results

3.It can solve problem of dimension reduction.

4.None of the above

Solution- 4.None of the above

Reason 1- This is because it organizes data by similarity and the input data know about the result. It is also used to solve dimensionality problem.

Reason 2- This is because it organizes data by similarity and therefore the input file realize the result. it’s also wont to solve dimensionality problem.

Q:3. Data Visualization is:

1.Used to communicate information clearly and efficiently to users by the usage of information graphics such as tables and charts.

2.Helps users in analyzing a large amount of data in a simpler way.

3.Makes complex data more accessible, understandable, and usable.

4.All of the above

Solution- 4. All of the above

Reason 1- All the reason that has been provided above are true. because Data visualization is a process of representing data into pictorial or graphical form.

Reason 2- All the rationale that has been provided above are true. because Data visualization may be a process of representing data into pictorial or graphical form.

Q:4. Data Visualization tool that can be used for displaying hierarchical data:

1.Histogram

2.Treemap

3.Scatter plot

4.Pie chart

Solution- 2.Treemap

Reason 1- Treemaps are visualizations for hierarchical data. They are made of a series of nested rectangles of sizes proportional to the corresponding data value.

Reason 2- Treemaps are visualizations for hierarchical data. they’re made from a series of nested rectangles of sizes proportional to the corresponding data value.

Q:5. Which of the following is a Regression problem?

1.Weather forecasting

2.Spam/Not-Spam emails categorization

3.Sentiment analysis

4.Fraud detection

Solution- 4. Fraud Detection

Reason 1- In order to effectively test, detect, validate, correct error and monitor control systems against fraudulent activities, businesses entities and organizations rely on specialized data analytics techniques such as data mining, data matching, sounds like function, Regression analysis, Clustering analysis and Gap.

Reason 2- In order to effectively test, detect, validate, correct error and monitor control systems against fraudulent activities, businesses entities and organizations believe specialized data analytics techniques like data processing , data matching, seems like function, multivariate analysis , Clustering analysis and Gap.

Q:6. Which of the following is a Classification problem?

1.Estimating the price of house

2.Credit/loan approval

3.Recommender system

4.Predicts the number of items which a consumer will probably purchase

Solution- 2. Credit/loan approval

Reason 1- Classification algorithms work by predicting the best group to which a data point belongs to by learning from labeled observations. It uses a set of input features for the learning process. Classification algorithms are good for grouping data that are never seen before into their various groupings and are therefore extensively used in machine learning tasks.

Reason 2- Classification algorithms work by predicting the simplest group to which a knowledge point belongs to by learning from labeled observations. It uses a group of input features for the training process. Classification algorithms are good for grouping data that are never seen before into their various groupings and are therefore extensively utilized in machine learning tasks.

Q:7. Decision tree:

1.Belongs to a family of unsupervised learning algorithms

2.Consider all attributes to split at each node, starting from the root node

3.Create a model that can be used to predict the class or value of the target variable by learning simple decision rules inferred from training data

4.All the above

Solution- 3.Create a model that can be used to predict the class or value of the target variable by learning simple decision rules inferred from training data

Reason 1- because it is used to create a model that can be used to predict the class or value of the target variable by learning simple decision rules inferred from training data

Reason 2- because it’s wont to create a model which will be wont to predict the category or value of the target variable by learning simple decision rules inferred from training data

Q:8. Bayesian Classifier:

1.Connects the degree of belief in a hypothesis before and after accounting for evidence

2.Uses conditional and marginal probability

3.Performance can be estimated using accuracy, precision, recall

4.All the above

Solution- 4.All the above

Reason 1- This is because Bayesian Classifier uses conditional and marginal probability. Also it connects the degree of belief in a hypothesis before and after accounting for evidence. And its performance can be estimated using accuracy, precision, recall. That is the reason all the above options are true.

Reason 2- This is because Bayesian Classifier uses conditional and marginal probability. Also it connects the degree of belief during a hypothesis before and after accounting for evidence. And its performance are often estimated using accuracy, precision, recall. that’s the rationale all the above options are true.

Q:9. When two clusters have a parent-child relationship then it is called as:

1.K-means clustering

2.Fuzzy c-means clustering

3.Hierarchical clustering

4.Density based clustering

Solution- 3.Hierarchical clustering

Reason 1- When two clusters have parent-child relationship or tree like structure then it is called as Hierarchical Clustering.

Q:10. Recommender system is an example of:

1.Clustering

2.Supervised learning

3.Reinforcement learning

4.Regression

Solution- 2. Supervised learning

Reason 1- The previous recommendation algorithms are rather simple and are appropriate for small systems. Until this moment, we considered a recommendation problem as a supervised machine learning task.

AKTU Artificial Intelligence For Engineering(KMC 101) Quiz Part 4