Train LightGBM

Train a LightGBM model using your data, where X is the input (as a DataFrame or NumPy array) and y is the target. You can also set sample weights in advanced options.

lightgbmfitclassificationregression

Required packages

You need below packages to use the code generated by recipe. All packages are automatically installed in MLJAR Studio.

lightgbm>=4.5.0

Interactive recipe

You can use below interactive recipe to generate code. This recipe is available in MLJAR Studio.

In the below recipe, we assume that you have following variables available in your notebook:

my_lightgbm (type LGBMClassifier)
my_regressor (type LGBMRegressor)
X (type DataFrame)
y (type Series)

Python code

# Python code will be here

Code explanation

This code fits model on training data (X, y). The training time depends on data size (number or rows and columns) and algorithm complexity.

LightGBM cookbook

Code recipes from LightGBM cookbook.

LightGBM Regressor
LightGBM Classifier
Train LightGBM
LightGBM Predict
Save LightGBM
Load LightGBM
LightGBM Importance

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