XGBoost Model in Python | Tutorial | Machine Learning

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How to create a classification model using XGBoost in Python? The tutorial will provide a step-by-step guide for this.

Timestamp:
00:00 - Introduction
01:02 - Import libraries and read data
02:49 - Create XGBoost classifier
06:31 - Evaluate model performance
07:54 - Hyper parameter tuning
11:32 - Result of hyper parameter tuning
13:59 - Build final XGBoost Model
15:05 - Performance as trees increase
16:34 - Feature Importance
17:11 - Find predictions for test data
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Clearly explained indeed, no fluff. Thanks Harish.

HitAndMissLab
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How does this not have more views!? Excellent video, EXACTLY what I needed to finish my project at work. This video could have saved me 10 hours of head scratching if I had seen it sooner.

mattc
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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

ThePaintingpeter
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all the advanced terms are simply described. Thanks, Harsh.

lxkhati
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Exactly what I needed. Explained very clearly. Thank You.

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

mosherchtman
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Really nice video and explanation Harsh

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

romaljaiswal
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This video covers a lot of thing in short time

harshchoudhary
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Good video sir, Thanks for making videos and educating us

kiranchowdary
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This is a a very well explained video !

MrLordmaximus
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Thanks for the video! Great learning experience.

Sam
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Thank you for the great content.
I'm wondering why don't you use early_stopping_rounds during grid search? That way you could set num_trees to a fixed big number (like you did later when building the final model) and don't have to grid search over it. Also, using your approach you probably overfit during grid search (due to the high number of estimators) and only get the best parameters when using all of the 1000, 2000 or 3000 trees.
In the final model, due to the fact that you use early_stopping_rounds, a different number of estimators will be used and therefore the optimal hyperparamters from the grid search are probably not the optimal hyperparameters for the final model. What do you think about it?

alexandergawrilow
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i have a doubt……during cross validation where we choose which model to use i am getting some accuracy but after hyperparameter tuning the accuracy jumps by 2 %

Is this normal?
This is in XGboost

pradyutmazumdar
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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.

fscode
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How can parallelization work in the Xgboost algorithm? Please explain it with an example

vhana
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hello sir as you have mention that you have more than 9 years of experience in data science and analytics field, so could you please tell me am i supposed to do dsa (data structures and algorithms ) for entry level data science role ?

thanos
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Thank you sir🙏, vidio ini sangat membantu 😊

riskamulliani
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I have a question about the Xgboost algorithm. The question is how parallelization works in the Xgboost algorithm and explain me with an example.

vhana
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How do you do it for Multiclass classification?

henilshah
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