Training SVM model in Python| Hyper-parameter Tuning|GridSearch CV |Support Vector Machines #3|

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In this video i cover how to train an svm model in python using sklearn library on the popular sklearn wine dataset.
Following topics are covered:
1) Data visualization with boxplot and histogram
2)Building SVM model
3)Hyper parameter tuning manually and with GridSearch CV

If you have any doubts leave it in the comments and thanks for watching !
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Great tutorial. Keep it up. One request, it will be better if you also store the code in your git repo and post the url in the description. It will be a lot of help for a lot of people.

pritishbanerjee
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got clear idea about hyper parameter tuning. really helpful thank you for the video. expecting more related videos

vikashchoubey
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best explanation available. keep going

ebyeldos
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Great Tutorial, Can I use this code for different dataset?

shannufarhath
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Love itt... Thanks broo for helping us

Immanuelllll-ihnr
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can we use cross validation along with grid search ?

yunicoardianpradana
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Really helpful and thanks for best explanation.

harshitasaxena
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I have a doubt, can scv be tuned by randomized searchCV instead of gridseaech

veeraazhagan
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can you explain me about what values of gamma can we take?

sureshmutyala
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if {'C': 10, 'degree': 1, 'kernel': 'linear'}
0.9259259259259259

Why C=10 is not shown in the Gridsearch?

mdmarufhossainkhan
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Y can't u keep code in description bro

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