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What is LightGBM library in Python?
LightGBM(LGBM) is an open-source gradient boosting library that has gained tremendous popularity and fondness among machine learning practitioners. It has also become one of the go-to libraries in Kaggle competitions. It can be used to train models on tabular data with incredible speed and accuracy.
Why is it called LightGBM?
LightGBM, short for Light Gradient Boosting Machine, is a free and open source distributed gradient boosting framework for machine learning originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks.
Where does the LightGBM Python module load data from?
The LightGBM Python module can load data from: The data is stored in a Dataset object. Many of the examples in this page use functionality from numpy. To run the examples, be sure to import numpy in your session. To load a LibSVM (zero-based) text file or a LightGBM binary file into Dataset: To load a scipy.sparse.csr_matrix array into Dataset:
Which is faster to load LIBSVM or LightGBM?
To load a LibSVM (zero-based) text file or a LightGBM binary file into Dataset: In LightGBM, the validation data should be aligned with training data. LightGBM can use categorical features as input directly. It doesn’t need to convert to one-hot encoding, and is much faster than one-hot encoding (about 8x speed-up).
How to solve init modeloption of LightGBM?
Can be solved using init_modeloption of lightgbm.train, which accepts one of two objects a filename of LightGBM model, or a lightgbm Booster object Code illustration:
Why does LightGBM not start on my computer?
2. On datasets with millions of features, training does not start (or starts after a very long time). 3. When running LightGBM on a large dataset, my computer runs out of RAM. 4. I am using Windows. Should I use Visual Studio or MinGW for compiling LightGBM?