Support Vector Machine Classifier using scikit-learnCoding Assignment2. Review the sample code for SVC.3. Use the Wine dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data) and add an SVClassifier(SVC), a random forest classifier with a depth of 2 and an Adaboost classifierand compare them using kfold cross validation with k=10. For the SVC, use the defaultsettings given in the sample code, use RFB kernel with C = 1.0.4. Plot all the accuracy results vs. each model (model type on the x-axis and accuracy onthe y-axis).5. Compare the results with the RBF kernel and the same value of C=1.06. Write down your observation on the comparison results.7. Plot the multi-class ROC curve and use the roc_auc_score function to calculate ROCscore.
Support Vector Machine Classifier using scikit-learnCoding Assignment2. Review the sample code for SVC.3. Use the Wine dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data) and add an SVClassifier(SVC), a random forest classifier with a depth of 2 and an Adaboost classifierand compare them using kfold cross validation with k=10. For the SVC, use the defaultsettings given in the sample code, use RFB kernel with C = 1.0.4. Plot all the accuracy results vs. each model (model type on the x-axis and accuracy onthe y-axis).5. Compare the results with the RBF kernel and the same value of C=1.06. Write down your observation on the comparison results.7. Plot the multi-class ROC curve and use the roc_auc_score function to calculate ROCscore.
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Support Vector Machine Classifier using scikit-learn
Coding Assignment
2. Review the sample code for SVC.
3. Use the Wine dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data) and add an SV
Classifier(SVC), a random forest classifier with a depth of 2 and an Adaboost classifier
and compare them using kfold cross validation with k=10. For the SVC, use the default
settings given in the sample code, use RFB kernel with C = 1.0.
4. Plot all the accuracy results vs. each model (model type on the x-axis and accuracy on
the y-axis).
5. Compare the results with the RBF kernel and the same value of C=1.0
6. Write down your observation on the comparison results.
7. Plot the multi-class ROC curve and use the roc_auc_score function to calculate ROC
score.
Coding Assignment
2. Review the sample code for SVC.
3. Use the Wine dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data) and add an SV
Classifier(SVC), a random forest classifier with a depth of 2 and an Adaboost classifier
and compare them using kfold cross validation with k=10. For the SVC, use the default
settings given in the sample code, use RFB kernel with C = 1.0.
4. Plot all the accuracy results vs. each model (model type on the x-axis and accuracy on
the y-axis).
5. Compare the results with the RBF kernel and the same value of C=1.0
6. Write down your observation on the comparison results.
7. Plot the multi-class ROC curve and use the roc_auc_score function to calculate ROC
score.
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