How do you interpret the prediction results? The results are all real numbers, can you look at each prediction on its own or do you have to evaluate as a whole? For instance person X target is 0.45, what does that tell me? Or negative values as the result what does that mean
Fantastic explanation! Your clear and engaging content has certainly earned you a new subscriber. I'm thrilled to have discovered your channel and I'm eager to see more insightful videos on Machine Learning. Keep up the incredible work! 💐
I get a warning at the training step. np.int has been deprecated and removed, so I can't continue as it doesn't run (no warnings that could be ignored). What do I need to solve this? Thanks.
Why not including euribor3m interest rates, it seems a strong predictor given the type of conversion for a bank, also it's proven in the data. Train 0.794 Test: 0.811
Then include it in your model. Choosing columns (or features) to include is just user judgement and domain knowledge, and so doesn't pertain much to making a better model in a mathematical sense since XGBoost is already so robust. If including it makes the model better, great put it in.
Love your calm explanation style and right level of detail for a youtube tutorial - thank you!
How do you interpret the prediction results? The results are all real numbers, can you look at each prediction on its own or do you have to evaluate as a whole? For instance person X target is 0.45, what does that tell me? Or negative values as the result what does that mean
Fantastic explanation! Your clear and engaging content has certainly earned you a new subscriber. I'm thrilled to have discovered your channel and I'm eager to see more insightful videos on Machine Learning. Keep up the incredible work! 💐
Love the tutorial and in depth explanation. Thanks
I get a warning at the training step. np.int has been deprecated and removed, so I can't continue as it doesn't run (no warnings that could be ignored). What do I need to solve this? Thanks.
Why do you use Real or Interger on your hyperparameters? Thanks!!!
Great staff
Great tutorial, but I have a question. Why did you change the result column to 0's and 1's if there's a target encoder? Can we keep them categorical?
What is F-Score here. Can you please explain the final step?
My model is not training. I mean programming is stuck at opt.fit(x_train,y_train) and it is not moving forward from here. What's Happening?
Love from China!
Thank kyo!
Why not including euribor3m interest rates, it seems a strong predictor given the type of conversion for a bank, also it's proven in the data.
Train 0.794
Test: 0.811
Hi Vincent, we didn't really focus on what features to include since this is more of a demo of the xgboost model:) Thanks for bringing it up
Then include it in your model. Choosing columns (or features) to include is just user judgement and domain knowledge, and so doesn't pertain much to making a better model in a mathematical sense since XGBoost is already so robust. If including it makes the model better, great put it in.
when i run "opt.fit(...)". It is wrong. "ValueError: multiclass format is not supported" How to fix it?
same here
u may need to read TargetEncoder documentation to find out more. He did not use sklearn onehot or ordinal encoder
@@langwang9130you have to set a parameter to specific xgb to use multiclasses
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The explanations kinda suck
really not well made at all, very frustratinfg to follow 4/10
till a devent tuorial tho!
@@andreibuchatskiy8472nah it’s horrible
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