![2a. Write a Python program to perform supervised classification on the Iris and Indian Pines datasets using Naive Bayes, Support Vector Machines (with RBF kernel), and Multi-layer Perceptron (MLP) classifiers for training sizes ={10%,20%,30%,40%,50%} for each of the below cases: i) With dimensionality reduction - Reduce data based on your choice of 'K' dimensions from 1a using each of the dimensionality reduction methods (PCA) followed by supervised classification by the listed classifiers. ii) Without dimensionality reduction - Data is followed by supervised classification using the listed classifiers. iii) Provide the plots for overall training accuracy, and overall classification accuracy versus the training size for all methods (classification schemes). Tabulate the classwise classification accuracies (i.e. extension of the sensitivity and specificity values) only for 30% training size over all methods for each dataset for case ii) i.e. without dimensionality reduction. Please note that the point distribution is as follows: - Total 4 plots, i.e. 2 plots for each dataset [case i and ii, overall training, and testing accuracy] - 2 tables (classwise accuracy), i.e, 1 table per dataset - Total points available: 30 points (5 points per plot/table)](https://gotit-pro.com/wp-content/uploads/2023/10/638be90e-4e3d-41db-a9d6-5279466b2f80.jpg "2a Write a python program to perform supervised classification on the Iris and Indian Pines")
Description
2a. Write a Python program to perform supervised classification on the Iris and Indian Pines datasets using Naive Bayes, Support Vector Machines (with RBF kernel), and Multi-layer Perceptron (MLP) classifiers for training sizes ={10%,20%,30%,40%,50%} for each of the below cases:
i) With dimensionality reduction – Reduce data based on your choice of ‘K’ dimensions from 1a using each of the dimensionality reduction methods (PCA) followed by supervised classification by the listed classifiers.
ii) Without dimensionality reduction – Data is followed by supervised classification using the listed classifiers.
iii) Provide the plots for overall training accuracy, and overall classification accuracy versus the training size for all methods (classification schemes). Tabulate the classwise classification accuracies (i.e. extension of the sensitivity and specificity values) only for 30% training size over all methods for each dataset for case ii) i.e. without dimensionality reduction.
Please note that the point distribution is as follows:
– Total 4 plots, i.e. 2 plots for each dataset [case i and ii, overall training, and testing accuracy]
– 2 tables (classwise accuracy), i.e, 1 table per dataset
– Total points available: 30 points (5 points per plot/table)
![1. Write a program called hist_eq which is run as follows: - Asks the user for an image - Opens the image as a grayscale image - Performs histogram equalization - Saves the result as a new image - Then displays the original image and the resulting image The program should apply global histogram equalization to the image. You may assume that the images are 8-bit images (grayscales are in the range [0,255]). Your program should be documented and organized appropriately. Include some images that you tested this on, both the before and afters. Ensure there is variation in intensity.](https://gotit-pro.com/wp-content/uploads/2023/10/608cefe4-cb34-4b5c-a766-9e36719e82ce.jpg)
![2. Write a program called "unsharp" which is run as follows: - Ask the user for an image. - Open the image as a grayscale image. - Pad the image (you choose the padding method). - Perform unsharp masking on the given image using the spatial domain and a 3x3 averaging mask. - Remove the padding. - Save the result as a new image. - Display the original image and the resulting image. The program should assume that the images are 8-bit images, with grayscale values in the range of [0, 255]. Ensure that the output intensity values stay within this range. Your program should be appropriately organized and documented. Include some example images that you have tested the program on, showing both the original and resulting images. Make sure there is variation in image detail and noise.](https://gotit-pro.com/wp-content/uploads/2023/10/c1bce0b4-8a19-454c-bef8-9964ac9cef03.jpg)
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