How to Implement Decision Trees in Python (Train, Test, Evaluate, Explain)

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In this video, we look into implementing Decision Tree algorithms with Python on a Jupyter Notebook using Scikit-learn. We look into how to change the decision tree algorithm settings, how we can use the explainability feature, how to apply early stopping and more.

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Can't believe this is free! it is much well explained comparing to what my lectures and tutor's did! Definitely recommended and Subscribed! Thank you so much!

xin
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Hi. I am studying this at the moment and your explanation is superb. You include what is relevant and what is useful without unnecessary deviation into obscurities or irrelevancies. Your explanation is perfect, Misra. Thankyou.

dk
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Walked away from my course learning material not understanding this and gained so much more from this video. Thanks!

johnturner
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Misra, thank you for this great "code-along". It really helped get a hands-on experience for the concepts that I'm learning.

Moiez
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thanks and Ramadan mubarek for all mosslims people

adnanemehdaoui
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Thank you so much!! Straight forward and easy to understand :)

m.riddle
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thank you Misra, you help me with my master thesis :)

horst
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thank you so muchhh Mrs!!! I found this video for hours...

blz_kanazaki
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Delivered in a friendly manner. Love it.

babatundeayinla
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How did you fo the code for feature importance i could not see line 21 properly

thebropill
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Wonderfully explained! Quite new to the data science and ML world and it's all so very exciting!

ZzZzZiggy
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Amazing explanation Misra. That unique smile on your face adds to the way you explain the complex things. I have subscribed to your channel. Thanks

rajamoorthy
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👉 Get real world data science experience by doing hands-on work

misraturp
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A huge thank you for your effort! I understood it easily and was able to do my assignment!!!

devcmd
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This is really a fantastic explanation. Such a great teacher! What tests are helpful in determining underfitting or overfitting?

ATothFTW
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By far the best explanation, thank you so much!

EB-chih
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Thanks Misra, you are expalining purely.

tarblood
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Is this actually a clustering algorithm only? How does the algorithm know what we are looking for as a target prediction, Misra? How is it possible that 'target' is a column of the 'data' but not included in the dataframe which again is based on 'data' from sklearn library?

jakobs
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Dear Misra, how could I conduct a multi-class prediction? Respectively what parameters would need to be changed to do so?

jakobs
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Quick question! 😊 Does the train_test_split function automatically remove the target variable from X_test, or should it be removed manually?

I followed along with your video and encountered something interesting. When I didn't specify max_depth and ran the model, I got an accuracy score of 100%. I'm a bit confused and wondering whether it's related to the target variable being present in the test set. Any insights or explanations would be greatly appreciated!

isitpokharel