Part 09 - Constructing Multi-Class Classifier Using SVM with Python

[@banderallogmany1912]

For lines 20 & 21, what is the benefit of using validation?

[@sabrinahuda7308]

can this be apply for multi-label classification too? if yes, where is the part i can change the algorithm to multi-label ?

[@curosaquila9995]

My jupyter code on line 2 pd.read_csv('iris_dataset.csv') error "FileNotFoundError". what yould i do?

[@kccchiu]

Nice video. I followed your tutorial and it worked brilliantly on my project.
On thing at the end, did you made a mistake on the confusion matrix? it seems like you are plotting predicted results against predicted results.

[@嗨您好-h1r]

yeah, i think he made a mistake on the confusion matrix, and i correct it into "cm = confusion_matrix(test_labels, y_pred)"
but i'am not really sure that is correct. XD

[@嗨您好-h1r]

Hello, your video is very good!
But may I ask a question? Is this multi-class classifier is using OVO(One vs. One) or OVA(One vs. All) or RCC?

[@LLFRA]

so no one-hot encoding is needed?

[@hggaming911]

If all data types in your dataset are integer or float so no need to use any encoding. Encoding is used for transfering your non-numircal data to numerical values because the model can work only with numbers.

[@beautyisinmind2163]

@@hggaming911 if i want to change the order, what is the code for example "Iris-setosa" = 3, "Iris-versicolor"=2, Iris-virginica" =1 instead of writing "Iris-setosa" = 1, "Iris-versicolor"=2, Iris-virginica" =3