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Readme
OrdinalGBT, which stands for Ordinal gradient boosted trees, is a Python package that implements an ordinal regression loss function using the lightGBM framework. Ordinal regression is a type of regression analysis used for predicting an ordinal variable, i.e. a variable that can be sorted in some order. LightGBM is a gradient boosting framework that uses tree-based learning algorithms and is designed to be distributed and efficient.
You can install OrdinalGBT using pip:
pip install ordinalgbt
Here are a few examples on how to use the LGBMOrdinal
class:
from ordinalgbt.lgb import LGBMOrdinal
import numpy as np
# Create the model
model = LGBMOrdinal()
# Generate some data
X = np.random.rand(100, 10)
y = np.random.randint(0, 3, 100)
# Fit the model
model.fit(X, y)
After fitting the model, you can use it to make predictions:
# Generate some new data
X_new = np.random.rand(10, 10)
# Use the model to make predictions
# the .predict method returns the class prediction rather than raw score or
# probabilities
y_pred = model.predict(X_new)
print(y_pred)
The predict_proba
method can be used to get the probabilities of each class:
# Use the model to predict probabilities
y_proba = model.predict_proba(X_new)
print(y_proba)
FAQs
A library to build Gradient boosted trees for ordinal labels
We found that ordinalgbt demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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