Contents
What is LightGBM regression?
Light Gradient Boosted Machine, or LightGBM for short, is an open-source library that provides an efficient and effective implementation of the gradient boosting algorithm. In this tutorial, you will discover how to develop Light Gradient Boosted Machine ensembles for classification and regression.
Can we use LightGBM for regression?
LightGBM default parameter for application is regression. Binary: It is used for binary classification. Multiclass: It is used for multiclass classification problems. Regression: It is used for doing regression.
Which is the correct NUM _ leaves for LightGBM?
Large num_leaves increases accuracy on the training set and also the chance of getting hurt by overfitting. According to the documentation, one simple way is that num_leaves = 2^ (max_depth) however, considering that in lightgbm a leaf-wise tree is deeper than a level-wise tree you need to be careful about overfitting!
When does LightGBM randomly select features on a tree?
LightGBM will randomly select a subset of features on each tree node if feature_fraction_bynode is smaller than 1.0. For example, if you set it to 0.8, LightGBM will select 80% of features at each tree node
When do you use LightGBM to represent categorical features?
The most common approach is to represent a categorical feature as it’s one-hot representation, but this is sub-optimal for tree learners. If you have high-cardinality categorical features, your tree needs to be very deep to achieve accuracy. LightGBM takes in a list of categorical features as an input so that it can deal with it more optimally.
What are the parameters of a dataset in LightGBM?
Dataset Parameters ¶ 1 max number of bins that feature values will be bucketed in 2 small number of bins may reduce training accuracy but may increase general power (deal with over-fitting) 3 LightGBM will auto compress memory according to max_bin. For example, LightGBM will use uint8_t for feature value if max_bin=255