CODEWITHSUNDEEP
machine-learningscikit-learnclassificationinit
Gabriel Gajardo
Gabriel Gajardo

Reputation: 65

KTBoost.BoostingRegressor TypeError: __cinit__() takes exactly 5 positional arguments (6 given)

I'm trying to run a BoostingRegressor model from the KTBoost package for binary classification, and i'm facing this error. I do not understand what it may cause it, nor any course of action I should follow to fix it. Is there something I can do to give more information? plshelpme

Xtrain = df.iloc[:, 2:492]
ytrain = df.Target
model = KTBoost.BoostingRegressor(loss='tobit', yl=0, yu=100)
model.fit(Xtrain, ytrain)

Upvotes: 0

Views: 209

Answers (1)

fabsig
fabsig

Reputation: 151

Thanks for pointing this out. This is now fixed as of version 0.1.14 of KTBoost. It was a wrong dependency on scikit-learn. Note that you need the newest version of scikit-learn (scikit-learn>=0.21.3).

In the future, please open an issue on https://github.com/fabsig/KTBoost. It will be answered faster there.

Upvotes: 1

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