| END TERM EXAMINATION | |
| Fifth Semester[BCA] | DECEMBER 2023 –January 2024 |
| Paper Code: BCAT-311 | Subject : Machine Learning with Python |
| Time:3 Hour | Maximum Marks:75 |
| Note: Attempt any five questions in all including Q. No.1 which is compulsory. Select one question from each unit. | |
| Q1 Write short notes on the following (Any Five) (5*5=25) | |
| (a) Explain different types of machine learning techniques. | |
| (b) Differentiate overfitting and underfitting problems encountered during machine learning. | |
| (c) What is the ROC curve? How is it constructed? | |
| (d) What do you mean by rule-based classification? | |
| (e) Explain logistic regression and its applications. | |
| (f) Write down the applications of Neural Networks. | |
| (g) Write a short note on Principal Component analysis. | |
| UNIT I | |
| Q2 (a) Consider two classes of classification problem of predicting whether a photograph contains a man or a woman. For the given test dataset of 10 records with expected outcomes and a set of predictions from a classification algorithm. (6) (i) Compute the confusion matrix for the data. (ii) Compute the accuracy, precision , recall, sensitivity, and specificity of the data.
| |
| b) Compare Multiclass Classification with Multilabel Classification. Write down appropriate examples to explain the difference. (6.5) | |
| Or | |
| Q3 (a) Under what circumstances are Precision, or Recall better performance metrics in comparison with accuracy? Give an example each for the situations where “Recall is a more important evaluation metric than Precision”.” Precision is a more important evaluation metric than Recall”. (6) | |
| (b) Explain simple linear regression. What do you mean by the least square method and Coefficient of Determination? (6.5) | |
| UNIT-II | |
| Q4 (a) What do you mean by Decision tree? How does the Decision tree algorithm work? Explain the attribute selection measure information gain. (6) | |
| (b) Explain the workimg of Naïve Bayes Classifier. For the dataset given below, check “If on a sunny day, Player can play the game?” with the help of frequency table and likelihood table. (6.5)
| |
| Q5 (a) What are ensemble learning models? Explain bagging and boosting in detail. (6) | |
| (b) Explain Support Vector Machine. Define the terms Hyperplane, Support Vectors, Kernel, Hard and Soft Margin. (6.5) | |
| UNIT-III | |
| Q6 (a) What is the role of the Activation functions in Neural Networks? List down the names of some popular Activation Functions used in Neural Networks. (6) | |
| (b) Explain the architecture of the Multilayer Feed-Forward Neural Network. (6.5) | |
| or | |
| Q7 (a) Explain Gradient Descent and its types. What are the different steps used in the Gradient Descent algorithm? (6) | |
| (b) What is perceptron and what are its basic components? How does perceptron work? (6.5) | |
| UNIT IV | |
| Q8 (a) Write down the algorithm for the K-means Clustering technique. What are the Distance Metrics used for quantitative and qualitative attributes? (6) | |
| (b)What do you mean by Feature selection? Explain Filter methods, Wrapper methods, and Embedded methods of feature selection. (6.5) | |
| or | |
| Q9 (a) Explain the Hierarchical Clustering technique and its types. Draw appropriate diagrams to explain the same. (6) | |
| (b)What are Self –Organizing Maps? How do they perform the weight update of the winning vector in the process of learning? (6.5) | |
