Load & check the data: breat_cancer dataset 8. Split your data into train 80% train and 20% test, use the last two digits of your student number for the seed. Build Classification Models Support vector machine classifier with linear kernel 9. Train an SVM classifier using the training data, set the kernel to linear and set the regularization parameter to C= 0.1. Name the classifier clf_linear_firstname. 10. Print out two accuracy score one for the model on the training set i.e. X_train, y_train and the other on the testing set i.e. X_test, y_test. Record both results in your written response. 11. Generate the accuracy matrix. Record the results in your written response.
Load & check the data: breat_cancer dataset 8. Split your data into train 80% train and 20% test, use the last two digits of your student number for the seed. Build Classification Models Support vector machine classifier with linear kernel 9. Train an SVM classifier using the training data, set the kernel to linear and set the regularization parameter to C= 0.1. Name the classifier clf_linear_firstname. 10. Print out two accuracy score one for the model on the training set i.e. X_train, y_train and the other on the testing set i.e. X_test, y_test. Record both results in your written response. 11. Generate the accuracy matrix. Record the results in your written response.
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
Problem 1PE
Related questions
Question
Load & check the data: breat_cancer dataset
8. Split your data into train 80% train and 20% test, use the last two digits of your student number
for the seed.
Build Classification Models
Supportvector machine classifier with linear kernel
8. Split your data into train 80% train and 20% test, use the last two digits of your student number
for the seed.
Build Classification Models
Support
9. Train an SVM classifier using the training data, set the kernel to linear and set the regularization
parameter to C= 0.1. Name the classifier clf_linear_firstname.
10. Print out two accuracy score one for the model on the training set i.e. X_train, y_train and the
other on the testing set i.e. X_test, y_test. Record both results in your written response.
11. Generate the accuracy matrix. Record the results in your written response.
Support vector machine classifier with “rbf” kernel
12. Repeat steps 9 to 11, in step 9 change the kernel to “rbf” and do not set any value for C.
Support vector machine classifier with “poly” kernel
13. Repeat steps 9 to 11, in step 9 change the kernel to “poly” and do not set any value for C.
Support vector machine classifier with “sigmoid” kernel
14. Repeat steps 9 to 11, in step 9 change the kernel to “sigmoid” and do not set any value for C.
(Optional: for steps 9 to 14 you can consider a loop)
By now you have the results of four SVM classifiers with different kernels recorded in your written
report. Please examine and write a small paragraph indicating which classifier you would recommend
and why
parameter to C= 0.1. Name the classifier clf_linear_firstname.
10. Print out two accuracy score one for the model on the training set i.e. X_train, y_train and the
other on the testing set i.e. X_test, y_test. Record both results in your written response.
11. Generate the accuracy matrix. Record the results in your written response.
Support vector machine classifier with “rbf” kernel
12. Repeat steps 9 to 11, in step 9 change the kernel to “rbf” and do not set any value for C.
Support vector machine classifier with “poly” kernel
13. Repeat steps 9 to 11, in step 9 change the kernel to “poly” and do not set any value for C.
Support vector machine classifier with “sigmoid” kernel
14. Repeat steps 9 to 11, in step 9 change the kernel to “sigmoid” and do not set any value for C.
(Optional: for steps 9 to 14 you can consider a loop)
By now you have the results of four SVM classifiers with different kernels recorded in your written
report. Please examine and write a small paragraph indicating which classifier you would recommend
and why
note:
programming language python
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