serengil / chefboost

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
https://www.youtube.com/watch?v=Z93qE5eb6eg&list=PLsS_1RYmYQQHp_xZObt76dpacY543GrJD&index=3
MIT License
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what is the method used to estimate the accuray rate and precision rate ? #12

Closed youcef-Ismael closed 3 years ago

youcef-Ismael commented 3 years ago

did you used the cross validation k fold or what is the method that you have used to calculate these rates ?

serengil commented 3 years ago

accuracy metrics are calculating for train set by default. But you can still calculate those metrics for your validation / test set as well. Cross validation is not covered yet.

from chefboost import Chefboost as cb
model = cb.fit(df, config)
cb.evaluate(model, validation_df)
macazaka commented 2 years ago

Is there a way to pass the accuracy level, label and confusion matrix to specific variables? Thank you.