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Thanks for the simple explanation, it would be interesting to see this applied on a more exhaustive case though (things like further explanation which hyperparameters to use in grid search or why one uses Gini or Entropy etc.). Thanks anyway :)
I am glad you liked the explanation. Thank you for taking the time to provide your feedback too :) Good idea. I am also figuring out what to make and not as I go. I will work on a follow up video and provide more details on it.
I was searching more for explanation of the grid search however wonderful example of application, but if you could really break down what the concept of the grid search is that would help me out so much more!
I know this is so vague, but for example.... I don't understand the hyper parameters you set and why they are important to the test, how do we read them... is this asking for too much perhaps?
Consider you are aiming at a Dart Board and the parameters required to hit the Bull Eye are (Your arm strength, your aiming skills, wind (imagine :P) and visibility. Now if each parameter were from a scale of 1-100, depending on your distance from the board you would have to calculate which combination works bets to hit the Bull's eye. You can start by trying a combination of (50, 60, 0, 40). But this combination did not hit the Bull's eye! Do you will keep trying all possible combinations till you hit it. This is what hyperparameter tuning is.
Is the hyper-parameter tuning just a trail and error method we use it on every ML model using different searching technique to improve performance? or else is there any test to identify whether it is required or not.
Hey Sir, I wanna ask something, i tuned my multiouput regression model (randomforest) and i get the best_score_ parameter. Is it means average score or sum score from my multioutput model? Thanks in advance. Sorry my bad english.
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Download the 6 -Step Strategy to master Data Science through now linear methods of learning
Download Link - kunaalnaik.com/learn-data-science-youtube/
Very informative!. It really helps me so much, i was wondering how to find the best parameter combinations when using hyperparameter tuning.
Thanks for the simple explanation, it would be interesting to see this applied on a more exhaustive case though (things like further explanation which hyperparameters to use in grid search or why one uses Gini or Entropy etc.). Thanks anyway :)
I am glad you liked the explanation. Thank you for taking the time to provide your feedback too :)
Good idea. I am also figuring out what to make and not as I go. I will work on a follow up video and provide more details on it.
Fantastic video - thanks!
Glad you enjoyed it!
Great video. Very clear and objective. Thanks.
I am glad you liked it!
I was searching more for explanation of the grid search however wonderful example of application, but if you could really break down what the concept of the grid search is that would help me out so much more!
I know this is so vague, but for example.... I don't understand the hyper parameters you set and why they are important to the test, how do we read them... is this asking for too much perhaps?
I will try in my next video :)
Consider you are aiming at a Dart Board and the parameters required to hit the Bull Eye are (Your arm strength, your aiming skills, wind (imagine :P) and visibility. Now if each parameter were from a scale of 1-100, depending on your distance from the board you would have to calculate which combination works bets to hit the Bull's eye. You can start by trying a combination of (50, 60, 0, 40). But this combination did not hit the Bull's eye! Do you will keep trying all possible combinations till you hit it. This is what hyperparameter tuning is.
I have a question here in order to tune the parameter how do we figure out the range for each hypermeters? is there any thumbs of rules to do so?
Your title is gridsearch, yet you also explain basic model preprocessing!
Hope this helps :)
Super u have explained very well
Thank you so much 🙂
I have two classes of data, sample of A:B is 4:1, which method would be best for me?
Use SMOTE to create a balanced class. They follow the process of modelling.
Excellent
Thank You!
How come you didn't split your data into validation data for hyper param tuning?
Hello sir, i can ask for u.... In get target data, y=data['target']
"in tolerance is not none :
Keyerror "target"
Can you share the column names of your dataset and the code you wrote? I am unable to get the full picture of the error.
Is the hyper-parameter tuning just a trail and error method we use it on every ML model using different searching technique to improve performance? or else is there any test to identify whether it is required or not.
GridSearchCV goes through, every parameter you provide and finds the best combination that give the best accuracy. You are right.
Thanks! I really thought tuning is a manual process :)
Even I thought the same! But now I am much smarter :p
I thought the video was about RandomizedSearchCV.Can you please show one for RandomizedSearchCV hyperparameter tuning?
You are right. Let me change the heading and also make another with RandomizedSearchCV! Thank you for providing feedback!
why you defined 'param_grid' but print 'random_grid' ?
Print(random_grid) ------ is showing me name 'random_grid' is not defined ? Plz help
Ensure you take the right feature name, or it might be because case sensitivity.
@@KunaalNaik thnxz buddy
Hey Sir, I wanna ask something, i tuned my multiouput regression model (randomforest) and i get the best_score_ parameter. Is it means average score or sum score from my multioutput model? Thanks in advance. Sorry my bad english.
Out of all the model scores, you get the score of the best model. Within Random forest trees you get the Average of the scores.
Why am I getting following error?
AttributeError: 'tuple' object has no attribute 'best_params_'
Can you please show the code you used to run? Through GitHub or Kaggle?
@@KunaalNaik thank Kunal . I fixed it
@@seelaswain8954 I am glad you got it!
can you share the datasets ?
why have you one ohe the data?
are Naik's born for ML🤣🤣
you didn't explain the concepts behind the code I think gpt is better than you
Yes, it is :) ChatGPT is always better to explain codes.