OrdinalGBT
Introduction
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.
Installation
You can install OrdinalGBT using pip:
pip install ordinalgbt
Usage
Here are a few examples on how to use the LGBMOrdinal
class:
- Fitting the model
from ordinalgbt.lgb import LGBMOrdinal
import numpy as np
model = LGBMOrdinal()
X = np.random.rand(100, 10)
y = np.random.randint(0, 3, 100)
model.fit(X, y)
- Predicting with the model
After fitting the model, you can use it to make predictions:
X_new = np.random.rand(10, 10)
y_pred = model.predict(X_new)
print(y_pred)
- Predicting probabilities with the model
The predict_proba
method can be used to get the probabilities of each class:
y_proba = model.predict_proba(X_new)
print(y_proba)