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!
you literally saved me , i have a project on cerrvical cancer prediction ML model and couldn't understand how to implement in code and how things come to be , you answered all the whys . Thank u soo much for this helpful video🤗
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.
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?
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!
Hey! Such an informative video. I just want to learn one thing, that is how do I enter a new list (consisting of only the features) and get my output as whether it is malignant or benign? Thanks a lot.
Thank you for the explanation. The feature importance plot depicts overall how important each feature is in distinguishing the two classes. Can we plot feature importance plot per class, one for malignant class and other for benign that shows feature importance score w.r.t each class, rather than whole?
Hey Ali, you are very welcome! That should be possible. I don't know off the top of my head how to do that but scikit-learn documentation should have this information.
Decision trees use supervised learning right? I don't understand at which point we tell the algorithm which is the correct data and which isn't (is the dataset already labelled)? Wouldn't we need to give the data and say this data = cancerous and give the other data and say this data = benign etc
Hey Ricardo, I'm not hosting the code anywhere, sorry :/ But everything I use in this video can easily be found on the scikit-learn documentationscikit-learn.org/stable/modules/tree.html
Hello guys, I’m not a student but have a question that I was hoping someone could help me with. Is there a minimum amount of data required per variable your testing when proceeding with this form of machine learning? Any guidance would be much appreciated
Hello, can you please help me. I'm using this decision tree model as a recommender system but my model can only recommend only one output. How can i recommend multiple outputs using only one sample data?
@@misraturp actually, we can't see the code of the sort_values function, not sure what are the parameters you provided inside. so far though great video, but would appreciate if you can help share .sort_values parameters inside at 18:03 onwards
ปีที่แล้ว
@@misraturp now i am stuck on the rest of the sort-value
Hey Voj, I do not have the code for this video anymore but you can find all the code on the scikit learn documentation for decision trees. scikit-learn.org/stable/modules/tree.html
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!
Glad it was helpful!
you literally saved me , i have a project on cerrvical cancer prediction ML model and couldn't understand how to implement in code and how things come to be , you answered all the whys . Thank u soo much for this helpful video🤗
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.
You're very welcome :)
Walked away from my course learning material not understanding this and gained so much more from this video. Thanks!
Misra, thank you for this great "code-along". It really helped get a hands-on experience for the concepts that I'm learning.
You're very welcome!
Thank you so much!! Straight forward and easy to understand :)
thank you Misra, you help me with my master thesis :)
thank you so muchhh Mrs!!! I found this video for hours...
You're very welcome :)
thanks and Ramadan mubarek for all mosslims people
You're welcome and Ramadan Mubarek. :)
Wonderfully explained! Quite new to the data science and ML world and it's all so very exciting!
Welcome to the AI world!
@@misraturp Thank you! It's, uh... it's also a bit overwhelming. ^^
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
Delivered in a friendly manner. Love it.
Thank you kindly!
A huge thank you for your effort! I understood it easily and was able to do my assignment!!!
Glad it helped!
thanks Misra!! great video
Tebrikler Mısra Hanım
Thanks Misra , you are expalining purely.
You're very welcome!
By far the best explanation, thank you so much!
Thank you!
Exactly!
Thank you Misra! great example
My pleasure!
This is really a fantastic explanation. Such a great teacher! What tests are helpful in determining underfitting or overfitting?
what an amazing video, thank you!!
Well explaination
best guide for beginners, keep it up
Thanks, will do!
Simple and clear!
Thanks!
Thank you!
thank you a lot and ramadan kareem
You're very welcome and happy Ramadan!
@@misraturp aww you're so nice thank you
How did you fo the code for feature importance i could not see line 21 properly
Awesome video, thank you Misra
You're very welcome Stephen!
Really great explanation, thank you!
You're very welcome :)
Very good explanation mam I like it
Great to hear!
Thanks for the video.
You're very welcome :)
Its really appreciated
👍
Excellent
Worderfull. Thanks
You're very welcome!
Dear Misra, how could I conduct a multi-class prediction? Respectively what parameters would need to be changed to do so?
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?
it seperates data in two sets then compares according to the input set and gives output on the basis we provided the input
Great video! subscribed to your channel. Good luck :)
Thank you :)
Great love ya!
Thanks!
I love you!
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!
Hello Misra, thanks for the excellent video. Any way you can make the Jupyter notebook available? Thanks.
It's been a while since I made this video so I don't have the code anymore unfortunately. :/
@@misraturp No worries. Thanks for replying 😃
@@PapiJack Of course!
Hey! Such an informative video. I just want to learn one thing, that is how do I enter a new list (consisting of only the features) and get my output as whether it is malignant or benign? Thanks a lot.
Also, it would be nice if you can link the jupyter notebook with this video.
Here it is: github.com/misraturp/Decision-Tree-Implementation
@@misraturp Thanks a lot! All the best for your work!
Thank you for the explanation. The feature importance plot depicts overall how important each feature is in distinguishing the two classes. Can we plot feature importance plot per class, one for malignant class and other for benign that shows feature importance score w.r.t each class, rather than whole?
Hey Ali, you are very welcome! That should be possible. I don't know off the top of my head how to do that but scikit-learn documentation should have this information.
Decision trees use supervised learning right? I don't understand at which point we tell the algorithm which is the correct data and which isn't (is the dataset already labelled)? Wouldn't we need to give the data and say this data = cancerous and give the other data and say this data = benign etc
Awesome video, where can I find the jupyter notebook?
Hey Ricardo, I'm not hosting the code anywhere, sorry :/ But everything I use in this video can easily be found on the scikit-learn documentationscikit-learn.org/stable/modules/tree.html
is it possible to traverse a decision tree based on user input and give a prediction? thx
Hello guys, I’m not a student but have a question that I was hoping someone could help me with. Is there a minimum amount of data required per variable your testing when proceeding with this form of machine learning? Any guidance would be much appreciated
Video is good but it must have been best with hyperparameter with crossvalidation.
Noted!
Hello, can you please help me. I'm using this decision tree model as a recommender system but my model can only recommend only one output. How can i recommend multiple outputs using only one sample data?
Miss can u show me how to use Information_gain source code in Dtree using sklearn library?? 🙂
Why am i getting accuracy score of 1?(i am using my own dataset after feature selection)
Maybe your model is overfitting?
how to implement it online
what do you mean by online friend?
In feature importances sort values are not appearing
Is the sort values function returning an error then?
@@misraturp actually, we can't see the code of the sort_values function, not sure what are the parameters you provided inside.
so far though great video, but would appreciate if you can help share .sort_values parameters inside at 18:03 onwards
@@misraturp now i am stuck on the rest of the sort-value
@ in sort_values write this in the code: .sort_values(0, ascending=False) that's all !
can you send to me your jupyter notebook you used?
Hey Voj, I do not have the code for this video anymore but you can find all the code on the scikit learn documentation for decision trees. scikit-learn.org/stable/modules/tree.html
can u drop the code
This video does not have accompanying code but you can find everything I used on the official scikitlearn Decision Trees documentation!
Best and clear explanation By the way I have a crush on you🙂🤣😂
please explain eda first
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