XGBoost banner

XGBoost

Created by: HardikRunwal
4 devlogs
3h 57m 19s

This shows how did i learnt and achieved to properly make XGBoost work all by myself , rawdogginggg itt!!

This project uses AI

For plotting the graphs , FOR THEE FRONT ENDD TOOOO

Demo Repository

README

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HardikRunwal
HardikRunwal worked on XGBoost
9 days ago
0h 28m logged

So basically i fixed , edited and hosted the XGBoost using streamlit , nothing special , i just know how to make models , so it was kinda new experience to me , lmk if any changes

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HardikRunwal
HardikRunwal worked on XGBoost
10 days ago
1h 5m logged

I agree i used AI to design the front end , but even that requires skills :D
Anyways here are some updates on that

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HardikRunwal
HardikRunwal shipped XGBoost
11 days ago

Shipped this project!

Hours: 2.39
Cookies: 🍪 24
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So this is a rawdogged and partially self-made XGBoost implementation , the thing that i find interesting is the mathematical importance behind it , as a math undergrad , i hope even you find that intereseting , the initial devpost just shows what and how does the math work here , it is really interesting please do check out , and let me know what should i improve coz i am here to improve and not to get the prizes only :D , i hope you really love it …. THANKSSS T_T

HardikRunwal
HardikRunwal worked on XGBoost
11 days ago
0h 52m logged

We brought machine learning :
Loaded the data set and got a feel for all the ingredients.
Split it into training and testing, keeping it honest.
Fired up an XGBoost classifier with a binary:logistic objective.
Trained the model, then made predictions on both labels and probabilities.
Tasted the confusion matrix for errors.
Tuned hyperparameters with GridSearchCV to spice things up.
Chosen the best model and checked it on unseen data.
It is running on enhanced trees that really bring the heat.

Dont judge my screenshot , i wasnt sure abt what to put

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HardikRunwal
HardikRunwal worked on XGBoost
17 days ago
1h 30m logged

So this isnt any technical stuff or someshit.. LOL … Here i just wanted to discuss the mathematical challenges and how did XGBoost work

  1. It is basically based on trees and depends how are we gettting the leaves of the trees
  2. we calculate Output and loss for every tree using calculations(formula based)
  3. It has manly 2 types XGBoost Regression and XGBoost Classification and both are almost similar , just have minute differences in the formulas
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