![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 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)
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