What it does

LightGBM is a gradient boosting framework that uses tree based learning algorithms.

For information about the algorithm and parameter settings please refer to the LightGBM website.

1 - Methods

There are two operations available:

1 - Train a model: A training set containing samples and their respective labels (or predicted values) are used as input. Based on the options selected, an estimator object is fitted to the data and is returned.

2 - Load a model and predict: An existing model is used to predict the class labels (or regression values) for a new dataset.

2 - Training input When you choose to train a model, you need a features dataset X and a labels set y. This tool expects tabular or sparse data for X and a single column for y (tabular). You can select a subset of columns in a tabular dataset as your features dataset or labels column. Below some examples are shown:

Sample tabular features dataset

The following training dataset contains 3 feature columns and a column containing class labels. You can simply select the first 3 columns as features and the last column as labels:

4.01163365529    -6.10797684314    8.29829894763     1
10.0788438916    1.59539821454     10.0684278289     0
-5.17607775503   -0.878286135332   6.92941850665     2
4.00975406235    -7.11847496542    9.3802423585      1
4.61204065139    -5.71217537352    9.12509610964     1

Sample sparse features dataset

In this case you cannot specify a column range.

4 1048577 8738
1 271 0.02083333333333341
1 1038 0.02461995616119806
2 829017 0.01629088031127686
2 829437 0.01209127083516686
2 830752 0.02535100632816968
3 1047487 0.01485722929945572
3 1047980 0.02640566620767753
3 1048475 0.01665869913262564
4 608 0.01662975263094352
4 1651 0.02519674277562741
4 4053 0.04223659971350601

2 - Training output

The trained model is generated and output in the form of a text file.

3 - Prediction input

When you choose to load a model and do prediction, the tool expects an already trained estimator and a tabular dataset as input. The dataset contains new samples for which you want to classify or predict values.

The number of feature columns must be the same in training and prediction datasets!

3 - Prediction output

The tool predicts the class labels for new samples and adds them as the last column to the prediction dataset. The new dataset (i.e. tabular input plus an additional column containing predicted values) is then returned as a tabular file. The prediction output format should look like the training dataset.