Decision Tree Hyperparam Tuning

Learn how to use Training and Validation dataset to find the optimum values for your hyperparameters of your decision Tree. Demonstrated for - Max Tree Depth and Min Sample Leaves hyper parameters.
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[@pzeid48]

Can you please specify the code from [59]?
It is not working for me. You say "data for x and data for y". Do you mean normal, test or train data?

[@machinelearningmastery]

ln[59] is for getting Total Absolute Error and a function. For any X, Y data that you pass it, it will calculate Total Absolute Error. When I said any X,Y, I mean - both for training X,training Y ; and also TestX, TestY. I have used it in ln [60]. Please go through ln [60] and that should clarify the usage of Total Absolute Error.

[@pzeid48]

@@machinelearningmastery Yes, thank you for the answer. I came to understand it shortly after posing the question.

[@asitnayak636]

Awesome video 🙌🏼

[@machinelearningmastery]

Thank you 🙌

[@pondskoi2073]

Could you please share the GitHub repo link