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(a) Based on the following number of hidden layers, (1) (ii) Experiment 1: Hidden layer=3 Experiment 2: Hidden layer=5 Experiment 3: Hidden layer=8 Experiment 4: Hidden layer=10 (iv) Fill in the accuracy, precision and recall rates for multilayer perceptron in Table 4 using WEKA software tool. Table 4: Results using WEKA for MLP Multilayer Perceptron Experiment 1 Experiment 2 Experiment 3 Experiment 4 Precision ? ? ? ? Recall ? ? ? ? Accuracy ? ? ? ? (b) Discuss the obtained results from Question 4(a)on its different parameters. (c) Run the given data set using 5-fold cross validation and 10-foldcross validation using the following classifier algorithms with DEFAULT parameters in the software: Support Vector Machine (i) (ii) Naive Bayes (iii) Decision Tree (148) You are required to tabulate on the results you obtained based on precision, recall and accuracy rates in Table 5. Table 5: Results using WEKA for different classifiers Support Vector Naive Bayes Decision Tree (148) Machine 5 10 5 10 5 10 Precision ? ? ? ? ? ? Recall ? ? ? ? ? ? Accuracy ? ? ? ? ? ? *Note: 5-5-cross validation & 10-10-cross validation (d) Analyse the differences of result output from Table 5 between 5-cross and 10-cross validations. And, describe how k-fold cross validation works. (e) State the best classifier based on results that have been obtained from Question 4 (a) and (c). Discuss how you can improve the accuracy rates of other classifiers.

(a) Based on the following number of hidden layers, (1) (ii) Experiment 1: Hidden layer=3 Experiment 2: Hidden layer=5 Experiment 3: Hidden layer=8 Experiment 4: Hidden layer=10 (iv) Fill in the accuracy, precision and recall rates for multilayer perceptron in Table 4 using WEKA software tool. Table 4: Results using WEKA for MLP Multilayer Perceptron Experiment 1 Experiment 2 Experiment 3 Experiment 4 Precision ? ? ? ? Recall ? ? ? ? Accuracy ? ? ? ? (b) Discuss the obtained results from Question 4(a)on its different parameters. (c) Run the given data set using 5-fold cross validation and 10-foldcross validation using the following classifier algorithms with DEFAULT parameters in the software: Support Vector Machine (i) (ii) Naive Bayes (iii) Decision Tree (148) You are required to tabulate on the results you obtained based on precision, recall and accuracy rates in Table 5. Table 5: Results using WEKA for different classifiers Support Vector Naive Bayes Decision Tree (148) Machine 5 10 5 10 5 10 Precision ? ? ? ? ? ? Recall ? ? ? ? ? ? Accuracy ? ? ? ? ? ? *Note: 5-5-cross validation & 10-10-cross validation (d) Analyse the differences of result output from Table 5 between 5-cross and 10-cross validations. And, describe how k-fold cross validation works. (e) State the best classifier based on results that have been obtained from Question 4 (a) and (c). Discuss how you can improve the accuracy rates of other classifiers.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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could you please do A,B,C,D and E

(a) Based on the following number of hidden layers, (1) Experiment 1: Hidden layer=3 Experiment 2: Hidden layer=5 Experiment 3: Hidden layer-8 Experiment 4: Hidden layer-10 (iv) Fill in the accuracy, precision and recall rates for multilayer perceptron in Table 4 using WEKA software tool. Table 4: Results using WEKA for MLP Multilayer Perceptron Experiment 1 Experiment 2 Experiment 3 Experiment 4 Precision ? ? ? ? Recall ? ? ? ? Accuracy ? ? ? ? (b) Discuss the obtained results from Question 4(a)on its different parameters. (c) Run the given data set using 5-fold cross validation and 10-foldcross validation using the following classifier algorithms with DEFAULT parameters in the software: (i) Support Vector Machine (ii) Naive Bayes (iii) Decision Tree (148) You are required to tabulate on the results you obtained based on precision, recall and accuracy rates in Table 5. Table 5: Results using WEKA for different classifiers Support Vector Naive Bayes Decision Tree (J48) Machine 5 10 5 10 5 10 Precision ? ? ? ? ? ? Recall ? ? ? ? ? ? Accuracy ? ? ? ? ? ? *Note: 5-5-cross validation & 10-10-cross validation I (d) Analyse the differences of result output from Table 5 between 5-cross and 10-cross validations. And, describe how k-fold cross validation works. (e) State the best classifier based on results that have been obtained from Question 4 (a) and (c). Discuss how you can improve the accuracy rates of other classifiers.
Transcribed Image Text:(a) Based on the following number of hidden layers, (1) Experiment 1: Hidden layer=3 Experiment 2: Hidden layer=5 Experiment 3: Hidden layer-8 Experiment 4: Hidden layer-10 (iv) Fill in the accuracy, precision and recall rates for multilayer perceptron in Table 4 using WEKA software tool. Table 4: Results using WEKA for MLP Multilayer Perceptron Experiment 1 Experiment 2 Experiment 3 Experiment 4 Precision ? ? ? ? Recall ? ? ? ? Accuracy ? ? ? ? (b) Discuss the obtained results from Question 4(a)on its different parameters. (c) Run the given data set using 5-fold cross validation and 10-foldcross validation using the following classifier algorithms with DEFAULT parameters in the software: (i) Support Vector Machine (ii) Naive Bayes (iii) Decision Tree (148) You are required to tabulate on the results you obtained based on precision, recall and accuracy rates in Table 5. Table 5: Results using WEKA for different classifiers Support Vector Naive Bayes Decision Tree (J48) Machine 5 10 5 10 5 10 Precision ? ? ? ? ? ? Recall ? ? ? ? ? ? Accuracy ? ? ? ? ? ? *Note: 5-5-cross validation & 10-10-cross validation I (d) Analyse the differences of result output from Table 5 between 5-cross and 10-cross validations. And, describe how k-fold cross validation works. (e) State the best classifier based on results that have been obtained from Question 4 (a) and (c). Discuss how you can improve the accuracy rates of other classifiers.
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