How to use num_leaves in LightGBM

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by dot icon Updated: Sep 26, 2023

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LightGBM is a flexible framework for machine learning. People use it for tasks such as classification and regression. You can use it in many fields, not just agriculture or construction.  

Applications of LightGBM:  

  • Finance and Banking  
  • Healthcare  
  • Retail and E-Commerce  
  • Marketing and Advertising  
  • Manufacturing and Industrial Processes  
  • Energy and Utilities  
  • Agriculture  
  • Construction and Real Estate  
  • Transportation and Logistics  

Benefits of Using LightGBM Systems:  

  1. Speed and Efficiency: LightGBM is known for being fast. It trains and predicts because it uses histograms and memory efficiently.  
  2. High Performance: It is when a machine is very good at doing tasks quickly and accurately. It can do this by using fewer trees.  
  3. Scalability: Using distributed computing, LightGBM can handle big data and large datasets. It scales well.  
  4. Categorical Feature Handling: The tool can handle categorical features. You don't have to do as much preprocessing.  
  5. Flexibility: LightGBM supports various aim functions and evaluation metrics, adapting to different tasks.  
  6. Regularization: It provides mechanisms for regularization to prevent overfitting and enhance generalization.  
  7. Interpretability: LightGBM provides feature importance scores and shape values to aid understanding.  
  8. Energy Efficiency: This algorithm saves energy using memory and processing things simultaneously.  


LightGBM systems help businesses make smart decisions by quickly analyzing complex data. This leads to better operations, focused marketing, and efficient resource use. Homeowners can use the framework to save energy. They can also predict maintenance and manage their homes more efficiently.  


LightGBM systems combine advanced machine learning with practical applications. The special features help businesses stay competitive in a data-driven world. They also help homeowners create intelligent, efficient living spaces. LightGBM systems demonstrate how data science can transform various industries and technologies.  

Fig: Preview of the output that you will get on running this code from your IDE.


In this solution we are using LightGBM library of Python.


Follow the steps carefully to get the output easily.

  1. Download and Install the PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install LightGBM - pip install LightGBM.
  4. Create a new Python file on your IDE.
  5. Copy the snippet using the 'copy' button and paste it into your Python file.
  6. Run the current file to generate the output.

I hope you found this useful.

I found this code snippet by searching for 'How do lightgbm encode categorial features?' in Kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. PyCharm Community Edition 2023.2
  2. The solution is created in Python 3.8 Version
  3. LightGBM v0.2.1 version.

Using this solution, we can be able to use Num leaves in LightGBM with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use Num leaves in LightGBM.

Dependent Library

LightGBMLSSby StatMixedML

Python doticonstar image 111 doticonVersion:v0.2.1doticon
License: Permissive (Apache-2.0)

An extension of LightGBM to probabilistic modelling and prediction


            LightGBMLSSby StatMixedML

            Python doticon star image 111 doticonVersion:v0.2.1doticon License: Permissive (Apache-2.0)

            An extension of LightGBM to probabilistic modelling and prediction

                      You can search for any dependent library on kandi like 'LightGBMLSS'.


                      1. What is lightgbm num_leaves, and how does it work in the Gradient Boosting Decision Tree?   

                      In LightGBM, num_leaves is a fancy word for a decision tree's biggest number of leaves. It influences the complexity and depth of individual decision trees in the ensemble. This parameter is only for LightGBM, and not all gradient-boosting programs have it.  


                      2. Are there any algorithms to tune hyperparameters for optimizing lightgbm's num_leaves value?  

                      Several hyperparameter tuning algorithms can optimize the value of LightGBM's num_leaves hyperparameter. Use these algorithms to find the best value for num_leaves. This will help improve your model's performance.  


                      3. How does the "light" part of the name influence the tree growth algorithm?   

                      LightGBM aims to be efficient in memory and computation. It doesn't sacrifice performance in gradient boosting.  

                      • Histogram-Based Learning  
                      • Leaf-Wise Growth  
                      • Gradient-Based One-Side Sampling  
                      • Exclusive Feature Bundling  
                      • Minimization of Splits  


                      4. What are some main light gum parameters, and how do they relate?   

                      LightGBM can improve its performance on different tasks and datasets with various settings. The parameters control training and gradient-boosting ensemble structure in various ways.  


                      5. How is decision tree learning related to light gum num_leaves parameter tuning?   

                      The tuning of LightGBM's num_leaves parameter relates to decision tree learning.  

                      Decision Tree Learning: In this learning, the internal node uses a feature to decide. Each leaf node represents the predicted outcome or class label. The depth of the tree and the number of leaves determine the complexity of the model.  


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