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

Machine Learning and Data Science Learning
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22 окт 2024

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Комментарии : 9   
@嗨您好-h1r
@嗨您好-h1r 3 года назад
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?
@banderallogmany1912
@banderallogmany1912 3 года назад
For lines 20 & 21, what is the benefit of using validation?
@kccchiu
@kccchiu 3 года назад
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
@嗨您好-h1r 3 года назад
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
@sabrinahuda7308
@sabrinahuda7308 3 года назад
can this be apply for multi-label classification too? if yes, where is the part i can change the algorithm to multi-label ?
@curosaquila9995
@curosaquila9995 Год назад
My jupyter code on line 2 pd.read_csv('iris_dataset.csv') error "FileNotFoundError". what yould i do?
@LLFRA
@LLFRA 3 года назад
so no one-hot encoding is needed?
@hggaming911
@hggaming911 3 года назад
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
@beautyisinmind2163 3 года назад
@@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
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