sklearn Logistic Regression hyperparameter optimization

[@KunaalNaik]

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[@rohitkhan1399]

Coolll !!!! Very simple and effective sir

[@Webenefit_Youtube]

Thank you so much for the video, very well explained

[@KunaalNaik]

Glad it was helpful!

[@apoorva3635]

Thanks. Crisp & clear explanation.

[@KunaalNaik]

Glad it was helpful!

[@18meiy13]

Thank you so much for the video, very well explained with simple words.

[@rishabhpanesar3683]

Very Helpful Video!
Thanks for posting this video! Helped a lot!!

[@KunaalNaik]

I am glad it helped!

[@ganeshprabhakaran9316]

Simple and neat explanation brother.. sounds great ..Kindly suggest Parameters for Voting classifier

[@KunaalNaik]

Hi Ganesh, I am opening a tight group of learners on whatsapp to actively engage and mentor. You can join if you are interested - bit.ly/DataScience_Kunaal_Whatsapp

[@KunaalNaik]

Will try another video :)

[@sudaniscience249]

you saved my life tonight

[@dok3820]

I appreciate your video sir...but may I ask how you chose what you wanted in the parameter grid?

[@sachink110]

Hello Kunaal, Nice informative video. Can I use Randomized search CV for decision tree/SVM/Logistic regression/NLP for checking hyperparameters? If yes, then the same parameter code needs to be put for all, or different parameters should be put in that hyperparameter selection bracket?

[@beautyisinmind2163]

same

[@amirhosseinrahimi3964]

Thanks, one queation, after tuining the model and finding the best hyper parameter, it is not necessary to run the model with found best parameter for moel training right? i mean after using GridSearchCV, model is already configured by best parameter? can you please elaborate?

[@KunaalNaik]

Yes, you should rerun the model with the best parameters. In this case, it just uses the best version for the model for doing calculations. (me being lazy :p)

[@amirhosseinrahimi3964]

@@KunaalNaik Thanks!
Si i chose a model, then i found the best parameter, then train the model with best parameter, and finally model.predict.. right?

[@KunaalNaik]

@@amirhosseinrahimi3964 You are right :) what model are you building?

[@amirhosseinrahimi3964]

@@KunaalNaik Thaks! in a classification problem, I am using Randomforest, Logistic Regression, and Knn to compare the f1 score...

[@KunaalNaik]

@@amirhosseinrahimi3964 Which one is best?

[@AaSinSin137]

Thank you so much... very much appreciated.

[@KunaalNaik]

Glad it helped

[@antonis7x]

Thank you so much!

[@CuteCreeperYT]

For a project
I removed multicollinearity and then dis hyperparameter tuning
Yet accuracy doesnt increase
Instead it decreased
Can jt happen?

[@vishnurajkr3769]

Input contains NaN, infinity or a value too large for dtype('float64'). i got an error like this

[@KunaalNaik]

You might want to do missing value treatment before you run this code. Check this code out - www.kaggle.com/funxexcel/titanic-solution-random-forest

[@zimelkhan8928]

Why you not passed scoring in gridsearchCV ??

[@KunaalNaik]

Most learners get confused about it when using it for the first time :)

[@ABDULLAHALATIQMARZUQIMARZUKI]

Question, I reached a penalty 11... so how do I fix that?

[@ibrahimalhassan5996]

What happens if one of the x variables is nominal, for example sex(male/female)?

[@KunaalNaik]

convert it to binary feature and then build the model.

[@sshahidmalik97]

hello sir, i almost used the same param_grid which you showed and i was working in Google Colab and GridSearchCV was fitting different combinations for about 3h 40m. it crashed at the end...what can or should i do to get the results?(my training dataset contained 17145 examples)... thanks

[@KunaalNaik]

Hi Shahid, try reducing the number of combinations. Take only two variations of each parameter and make it run.

[@rawindersingh4007]

Hi there, I am not seeming to get more optimal performance using this, Do you have any idea why?

[@KunaalNaik]

You need to apply these strategies:
1/ Outlier Detection and imputation
2/ Missing Value Imputation Strategy
3/ Transformation Strategy
4/ Cross-Validation Framework
Iterate by changing the above first and check your performance.

[@rawindersingh4007]

@@KunaalNaik thank you 😇

[@vishnurajkr3769]

i got error as "Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty".

[@KunaalNaik]

lbfgs cannot be used with L1 Penalty, that why the error. When you use lbfgs you could remove L1 from it. For others you could use L1.

[@กฤตนนทองสุทธิ์]

Thanks

[@varuntandon4465]

Could hyperparameter tuning cause over fitting on the training data? Shouldn't you use a subset of the data to tune your hyperparameters (a validation set)?

[@KunaalNaik]

Agree, you should use a validation set. That works for a some models like XGBoost, LGB etc... for basic model we just tune with parameters.

[@harshverma574]

What am I doing wrong if after gridsearchcv my accuracy for the model is decreasing!

[@KunaalNaik]

Stir the range of the parameters. It takes a while to get the sweet spot :)

[@varunchakilam1294]

failed to perform for categorical data
movie review classifier

[@KunaalNaik]

Does it have more than one category? If Yes, then try some other algorithm.

[@chetanmazumder310]

Why can't see the hyper parameters after dot fit ?

[@KunaalNaik]

Could not get you Chetan?

[@Mustistics]

I thought you'd explain the meaning of the hyperparams.

[@7polletes]

This is much better than that. Amazing trick.

[@arpitakar3384]

reached haha

[@arpitakar3384]

use hashtags