Machine Learning Algorithms Class 11 NCERT Solutions – The CBSE has updated the syllabus for St. XI (Code 843). The NCERT Solutions are made based on the updated CBSE textbook. All the important information is taken from the Artificial Intelligence Class XI Textbook Based on the CBSE Board Pattern.
Machine Learning Algorithms Class 11 NCERT Solutions
A.Multiple Choice Questions
1. Which of the following are the types of correlation?
a. Positive correlation
b. Negative Correlation
c. No correlation
d. All of the above
2. Which of the following techniques is an analysis of the relationship between two variables to provide the prediction mechanism?
a. Standard error
b. Correlation
c. Regression
d. None of the above
3. Which of the given plots is suitable for testing the linear relationship between a dependent and independent variable?
a. Bar chart
b. Scatter plot
c. Histograms
d. All of the above
4. Which of the following scatter plots represents a positive correlation?
a. points scattered randomly with no apparent trend
b. points forming a diagonal line and bottom left to top right
c. points forming a diagonal line from top left to bottom right
d. points clustered around a central point
5. Which regression technique is used when there is only one independent variable?
a. logistic regression
b. multiple linear regression
c. simple linear regression
d. polynomial regression
6. What is one advantage of linear regression analysis?
a. it is robust to outliers
b. it can capture nonlinear relationships between variables
c. it is simple and easy to interpret
d. it is suitable for classification tasks
7. What is supervised learning in Artificial Intelligence?
a. training a computer algorithm on input data that is not labelled.
b. training a computer algorithm on input data that has been labelled for a specific output.
c. training a computer algorithm without any input data
d. training a computer algorithm to perform unsupervised tasks.
8. Which type of classification involves categorizing data into two distinct classes?
a. multi-class classification
b. binary classification
c. unsupervised classification
d. regression classification
9. What is logistic regression commonly used for in binary classification?
a. categorizing observations into multiple classes
b. predicting continuous values for input data
c. categorizing observations into two distinct classes
d. identifying unstructured data patterns
10. What is the primary goal of classification in AI?
a. categorizing data into random groups
b. locating and classifying things or concepts into predefined groups
c. predicting continuous values for input data
d. identifying unstructured data patterns
11. Which algorithm is commonly used for binary classification?
a. Decision trees
b. Support Vector Machine
c. Logistic Regression
d. k-Nearest Neighbors
12. The K-Nearest Neighbors (KNN) algorithm assigns a class to new data point by considering:
a. Distance from the data point to a predefined decision boundary
b. Majority vote of its K nearest neighbors in the training data
c. Similarity of the data point to a cluster centroid
d. probability of each class given the data point’s features.
13. What does a classification model in AI ultimately want to achieve?
a. to identify patterns and associations in data
b. to predict continuous numerical values
c. to categorize input data into predefined classes or labels
d. to optimize decision-making processes
14. What are some challenges in applying classification models to real-world problems?
a. Data bias and fairness
b. Interpretability and explainability
c. overfitting and underfitting
d. All of the above
15. What is clustering?
a. Grouping labeled dataset
b. Dividing data into different clusters
c. Finding linear association between variables
d. Predicting future behaviors of a dependent variable
16. Which type of learning does clustering belong to?
a. Supervised learning
b. Unsupervised learning
c. Semi-supervised learning
d. Reinforcement learning
17. Which method is used to group highly dense areas into clusters?
a. Partitioning clustering
b. Density-based clustering
c. Distribution model-based clustering
d. Hierarchical clustering
18. Which algorithm is an example of partitioning clustering?
a. Mean-shift algorithm
b. DBSCAN algorithm
c. K-Means algorithm
d. Fuzzy clustering algorithm
19. Which clustering method allows data objects to belong to more than one group or cluster?
a. Partitioning clustering
b. Density-based clustering
c. Distribution model-based clustering
d. Fuzzy clustering
20. Which clustering algorithm is sensitive to outliers?
a. K-Means algorithm
b. Mean-shift algorithm
c. DBSCAN algorithm
d. Hierarchical clustering
B. Fill in the blanks
1. In _ type of ML, the models are not trained in labeled data sets.
Show Answer ⟶2. The _______ measures the linear relationship between the independent and dependent variables.
Show Answer ⟶3. _________________predicts continuous numerical values, while Logistic regression predicts discrete categories.
Show Answer ⟶4. _ are data points on the scatterplot that do not follow the pattern of the dataset.
Show Answer ⟶5. ___ algorithm operates based on the principle of proximity, making predictions by considering the similarity between data points.
Show Answer ⟶6. Clustering is a machine learning technique used to group __ dataset.
Show Answer ⟶7. Partitioning clustering divides the data into non-hierarchical groups, also known as __ method.
Show Answer ⟶8. Density-based clustering connects highly dense areas into clusters, separated by areas of __.
Show Answer ⟶9. The primary requirement for the number of clusters in K-Means algorithm is __ beforehand.
Show Answer ⟶10. Clustering is widely used in applications such as market segmentation and __.
Show Answer ⟶C. True or False:
1. Clustering is a supervised learning technique.
Show Answer ⟶2. Hierarchical Clustering requires pre-specifying the number of clusters.
Show Answer ⟶3. Fuzzy clustering is a hard clustering method.
Show Answer ⟶4. Classification is an unsupervised learning technique.
Show Answer ⟶5. In k-NN algorithm, k is the number of nearest data points.
Show Answer ⟶6. K-Means algorithm requires specifying the number of clusters.
Show Answer ⟶D. Short answer type questions:
1. What is Machine learning? Name the three methods of machine learning.
Answer: Machine Learning (ML) is a part of artificial intelligence (AI) that focuses on teaching computers to learn from data and make decisions without being explicitly programmed. The three method of machine learning are-
- Supervised Learning: Supervised learning involves the model learning from labeled data, where the input data is accompanied by the correct output.
- Unsupervised learning: Unsupervised learning, on the other hand, deals with unlabelled data, where the algorithm tries to find hidden patterns or structure without explicit guidance.
- Reinforcement learning: Reinforcement learning involves an agent learning to make decisions by interacting with an environment to maximize cumulative rewards.
2. How are correlation measures used in AI applications?
Answer: correlation is a fundamental concept that helps us grasp the relationship between variables, laying the groundwork for predictive modeling and insightful analysis. Correlation is a measure of the strength of a linear relationship between two quantitative variables (e.g. price, sales). If the change in one variable appears to be accompanied by a change in the other variable the two variables are said to be correlated, and this inter dependence is called correlation.
3. Name some examples of regression algorithms?
Answer: Regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. For example, the relationship between height and weight, temperature, salary etc.
4. What are regression algorithms used for?
Answer: Regression analysis is particularly useful when dealing with continuous data, where variables can take on any value within a certain range. For example, variables such as height, temperature, salary, and time are all continuous, meaning they can be measured along a continuous scale.
5. What is Linear regression? Give two applications of regression in machine learning?
Answer: Linear regression is one of the most basic types of regression in machine learning. The linear regression model consists of a predictor variable and a dependent variable related linearly to each other.
Applications of Linear Regression:
- Market Analysis: Linear regression helps understand how different factors like pricing, sales quantity, advertising, and social media engagement relate to each other in the market.
- Sales Forecasting: It predicts future sales by analyzing past sales data along with factors like marketing spending, seasonal trends, and consumer behavior.
6. How can outliers impact regression analysis?
Answer: Outliers, or extreme data points, can disproportionately influence the regression model and lead to inaccurate predictions. In the presence of outliers, it is essential to assess their impact and consider alternative modeling approaches.
7. What is the primary difference between classification and regression?
Answer: The difference between classification and regression are classification predicts discrete categorize or labels like spam or not spam in Gmail but regression predicts continuous values like temperature of price.
8. Provide examples of classification problems in real-life scenarios.
Answer: Classification problem in real-life scenarios, let us say, you live in a gated housing society and your society has separate dustbins for different types of waste: paper waste, plastic waste, food waste and so on. What you are basically doing over here is classifying the waste into different categories and then labeling each category.
9. What are some common applications of clustering techniques?
Answer: Some of the common clustering techniques include social media, marketing, image process etc.
10. List the types of clustering methods.
Answer: There are four types of clustering methods.
- Partitioning Clustering: It is a type of clustering that divides the data into nonhierarchical groups.
- Density-Based Clustering: The density-based clustering method connects the highly dense areas into clusters, and the arbitrarily shaped distributions are formed as long as the dense region can be connected.
- Distribution Model-Based Clustering: In the distribution model-based clustering method, the data is divided based on the probability of how a dataset belongs to a particular distribution.
- Hierarchical Clustering: Hierarchical clustering can be used as an alternative for the partitioned clustering as there is no requirement of pre-specifying the number of clusters to be created.
E. Long Answer Questions:
1. How does classification model work?
Answer: In the supervised machine learning technique of classification, the model attempts to predict the appropriate label for a given input set of data. In classification, the model is fully trained on training data, then tested on test data before being applied to new, unseen data to make predictions.
2. Explain the types of clustering.
Answer: There are four types of clustering methods.
- Partitioning Clustering: It is a type of clustering that divides the data into nonhierarchical groups.
- Density-Based Clustering: The density-based clustering method connects the highly dense areas into clusters, and the arbitrarily shaped distributions are formed as long as the dense region can be connected.
- Distribution Model-Based Clustering: In the distribution model-based clustering method, the data is divided based on the probability of how a dataset belongs to a particular distribution.
- Hierarchical Clustering: Hierarchical clustering can be used as an alternative for the partitioned clustering as there is no requirement of pre-specifying the number of clusters to be created.
3. Write any two advantages and disadvantages of linear regression.
Answer:
Advantages of Linear regression
- Simple technique and easy to implement
- Efficient to train the machine on this model
Disadvantages of Linear regression
- Sensitivity to outliers, which can significantly impact the analysis.
- Limited to linear relationships between variables.
4. What are the steps involved in k-NN algorithm?
Answer: The primary steps make up the k-NN algorithm’s operation are:
Select the number K of the neighbors
- Calculate the Euclidean distance of K number of neighbors
- Take the K nearest neighbors as per the calculated Euclidean distance.
- Among these k neighbors, count the number of the data points in each category.
- Assign the new data points to that category for which the number of the neighbor is maximum.
- Our model is ready.
5. What are the steps involved in k-means clustering?
Answer: The working of the K-Means algorithm is explained in the below steps:
- Select the number K to decide the number of clusters.
- Select random K points or centroids. (It can be other from the input dataset).
- Assign each data point to their closest centroid, which will form the predefined K clusters.
- Calculate the variance and place a new centroid of each cluster.
- Repeat the third steps, which means reassign each datapoint to the new closest centroid of each cluster.
- If any reassignment occurs, then go to step-4 else go to FINISH.
- The model is ready.
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