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Train, Test, & Validation Sets | How to Train Machine Learning Models (Properly!!!)

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The Notebook: colab.research...
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Опубликовано:

27 авг 2024

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Комментарии : 28

@GregHogg Год назад

Take my courses at mlnow.ai/!

@djjiang3718 2 года назад

Just want to say Thank you, Greg! love your content always good stuff! Keep up with hard work! Thanks for teaching and sharing !

@GregHogg 2 года назад

I really appreciate this, thanks so much and you're very welcome 😄😄

@tartlord7061 Год назад

Great and easy to follow video, thanks!

@PeterPan-xp2gl Год назад

Hey Greg, thanks for your video! Could you explain why you convert the dataframe to a numpy matrix? Since the whole procedure also works with data frames?

@ismailucuran4523 Год назад

probably its because he didn't use scikitlearn treain_test_split

@panagiotisgoulas8539 2 года назад

Cool. thanks for that.

@user-ip9zo8zp4c Год назад

Is it possible that you wrote the arguments of 'mean_absolute_error' in the wrong order?

@natureclipsreal Год назад

If I am using cross validation during the training process, can I still use a validation set after training the model?

@arsheyajain7055 2 года назад

Oooh awesome!!

@jyotikapatil2354 Год назад

Only video explaining actual hyperparameter tuning using validation test.

@nayliafiqah4072 Год назад

Thank you so much;

@GregHogg Год назад

You're very welcome!!

@mayreyes4402 Год назад

when I try to find mean absolute error for linear_test_ preds the result when I run it is "ValueError: could not convert string to float: 'KS'" what should I do with that?

@itowindsor 3 месяца назад

One of your feature might be a categorical one. Therefore your models can't work with it. One way to overcome it is to one hot encode this feature. It will increase the dimension of your features spaces (as you will have one more dimension for each category) but every algorithm will understand it