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How does LightGBM work?
While other algorithms trees grow horizontally, LightGBM algorithm grows vertically meaning it grows leaf-wise and other algorithms grow level-wise. LightGBM chooses the leaf with large loss to grow. It can lower down more loss than a level wise algorithm when growing the same leaf.
How do I increase my LightGBM accuracy?
In order to get better accuracy, one can use a large max_bin , use a small learning rate with large num_iterations , and use more training data. One can also use many num_leaves , but it may lead to overfitting. Speaking of overfitting, you can deal with it by: Increasing path_smooth.
Can you run light GBM with L1 and L2 regularization?
OR should I run with all the features & the LGBM algorithm (with L1 & L2 regularization) will take care of low importance features and won’t give them any weight or may be give minute weight when it makes predictions. Any help will be highly appreciated. With regularization, LightGBM “shrinks” features which are not “helpful”.
Which is better, LightGBM level wise or leaf fixed?
Holding #leaf fixed, leaf-wise algorithms tend to achieve lower loss than level-wise algorithms. Leaf-wise may cause over-fitting when #data is small, so LightGBM includes the max_depth parameter to limit tree depth.
How does LightGBM work compared to other algorithms?
While other algorithms trees grow horizontally, LightGBM algorithm grows vertically meaning it grows leaf-wise and other algorithms grow level-wise. LightGBM chooses the leaf with large loss to grow. It can lower down more loss than a level wise algorithm when growing the same leaf.
How to store weight data in LightGBM file?
Weight Data ¶. LightGBM supports weighted training. It uses an additional file to store weight data, like the following: 1.0 0.5 0.8 It means the weight of the first data row is 1.0, second is 0.5, and so on. The weight file corresponds with data file line by line, and has per weight per line.