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so clear explanation- thanks harsh - pray you do great in life :))

This guy is a real deal. Thanks bro

Thanks boss

Nice Tutorial to start with ML Flow

Keep it up brother, your videos are clean. I had lost hope in Indian youtubers after all the clickbait. But yours are real content.

To much fast bro

Lovely walkthrough! I really wanted to see the interface before getting started with MLFlow

the shortest yet clearest model walk thru ever. kudos bruh

Thank you sir🙏, vidio ini sangat membantu 😊

Hi, how to host the mlflow server in say a redhat vm and then use that url as our set_tracking_uri lets say from a windows vm??

eval_metric throws error, can anyone suggest me the reason?

train() got an unexpected keyword argument 'early_stopping_rounds' i get this error

Hello, been long time no videos, i hope to see more videos from you.

unable to install 'imghdr' it says "Could not find a version that satisfies the requirement imghdr (from versions: none)"

This helped!! Thank you so much.

Hii..actually I trained tflite model and apply mlflow on google colab but autolog is not working and also(mlflow.tensorflow.log_model) is not working...how can I do that??

Nice One.

I think im the stupid one ... the video is in detail but i fail to do ... head scratching moment in spyder :(

Ruff over flake

Great demo and walkthrough!

Really nice video and explanation Harsh

Thank you

More videos [like this] that teach optimization of all the parameters in the model, please

Thanks

I cannot overstate the fact that this video is really clear and terrific. Absolutely fantastic effort on your part. Thank you very much for doing this

can you make video for light gbm for prediction?

Why is the prediction outputs not binary [0,1], but continuous values? Does it represent a confidence in something being a 1?

Thank you. Well explained! I'm wondering why don't you use a regressor model instead of the classifier model, because what I see are numerical datasets. I wish you can provide/ explain plots that illustrate observations vs. predictions, and the statistical performance indicators such as RMSE, residuals (errors), R2, etc. Lastly, can I use transfer learning method in the random forest method or it is just limited for deep neural networks (freezing weights)? Thank you so much!

Great video thank you!

Just wanted to know whether EDA, feature selection is not needed for XGboost ?

how to get class predictions instead of probability prediction ?

Thank's for your video, I have questions about "min_samples_split" and "min_samples_leaf" you have divided a thousand (/1000), where did you get it from the number 1000?

Disliking this video because it’s too good and I don’t want others to know abt it 😂😂

in my project only i get 45% in training and 44 in testing. What do you think i can do to get better accuracy please.

This video covers a lot of thing in short time

Sir, your videos or so good and clear. Can you please highlight how evaluation metrics could be applied to test the outcomes of validation from Random forest. Thank you

Nice video! Thank you so much! One pair of doubts, is there a way to download the notebook with outputs from Kaggle? Is it possible to train models like XGBoost with GPU? because the last time I tried there, the debugger suggested that it was only possible with sequential models like neural networks.

The program is too too much time to run 😵 But Thanks to you Sir, for explaining the program and arguments very well.

extremely clear, short, and useful !! tks

This is a a very well explained video !

ohh i see thanks man

I am getting the following error NameError Traceback (most recent call last) Input In [6], in <cell line: 11>() 7 print(os.path.join(dirname, filename)) 9 # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 10 # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session ---> 11 kaggle/input/santander-customer-transaction-prediction/sample_submission.csv() 12 kaggle/input/santander-customer-transaction-prediction/train.csv() 13 kaggle/input/santander-customer-transaction-prediction/test.csv() NameError: name 'kaggle' is not defined

ok...then after that how to create confusin matrix,classification report

why you have not fit the model ?

Thanks man, great explanation ^^

Thank you, Mr. Kumar.

Thank you so much for this work. How could you plot the chart between R-squared and number of estimators from 20-200?

please do not stop making videos.you are too good

Your justification for learning rate is not right.

How do you do it for Multiclass classification?