LightGBM is a powerful open-source gradient-boosting framework. It's designed to handle large datasets. Its speed and memory usage level are high.
LightGBM uses a technique called gradient boosting. It combines many weak learners (decision trees) to create a strong predictive model. LightGBM is a gradient-boosting framework. It uses tree-based learning algorithms.
It is designed with the following advantages:
- Faster training speed and higher efficiency.
- Lower memory usage.
- Better accuracy.
- Support of parallel and GPU learning.
- Capable of handling large-scale data.
Light GBM is a new algorithm. It has a list of parameters. The size of the dataset is increasing. It becomes very difficult for data science algorithms to make accurate results. Light GBM earns the nickname Light for its high speed. Light GBM can manage the large size of data. It takes less memory to run. Light GBM focuses on the accuracy of results. LGBM also supports GPU learning. So, data scientists are using LGBM for data science application development. It is not preferable to use LGBM on small datasets. But Light GBM is sensitive to overfitting. So that can easily overfit small data.
(LightGBM)Light Gradient Boosting Machine is a Machine Learning library. It provides algorithms under a gradient-boosting framework. Microsoft developed it. Three more GB variants are LightGBM, XGBoost (eXtreme), and CatBoost (Categorical Boosting). We compared them with GB and rrBLUP in parallel. It has better accuracy than other boosting algorithms. It handles overfitting while working with smaller datasets. To build a LightGBM training container, use the LightGBM built-in algorithm.
The LightGBM algorithm uses Gradient-Based one-side sampling (GOSS) and Exclusive Feature Bundling (EFB). It allows the algorithm to run faster while maintaining high accuracy. It has a machine learning algorithm with a tabular kind of data. Both regression and classification problems use this. GBT, GBDT, GBRT, GBM, and MART are fast, distributed, high-performance gradient-boosting frameworks. People use this for ranking, classification, and many other machine-learning tasks.
These techniques satisfy the limitations of the histogram-based algorithm. All GBDT (Gradient Boosting Decision Tree) frameworks use it. LightGBM is an ensemble learning framework. It adopts the strategy of leaf-wise tree growth. It is to create decision trees and ultrafast in coping with large datasets. LightGBM selects only the most important features and data instances. This is to compute the gradients during the training process. Also, this makes it faster than other gradient-boosting frameworks.
It supports distributed training on many machines. It makes it easy to scale up for large datasets. GB traverses all the features to select important nodes. When we build trees, we base the prediction on high effectiveness. If the validation data is not declared, 20% of your training data is randomly sampled. This is to serve as the validation data. The distribution and efficiency of the design. Also, faster drive speed, higher efficiency, lower memory usage, and better accuracy.
Preview of the output that you will get on running this code from your IDE.
In this solution, we used the Light GBM and SciKit-Learn libraries.
Follow the steps carefully to get the output easily.
- Download and Install the PyCharm Community Edition on your computer.
- Open the terminal and install the required libraries with the following commands.
- Install Light GBM - pip install Light GBM.
- Install SciKit-Learn - pip install SciKit-Learn.
- Create a new Python file on your IDE.
- Copy the snippet using the 'copy' button and paste it into your python file.
- Write from sklearn.datasets import data in line no. 2
- Run the current file to generate the output.
I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.
I found this code snippet by searching for ' LightGBM - Module not callable' in Kandi. You can try any such use case!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in PyCharm 2022.3.
- The solution is tested on Python 3.11.1
- Light GBM version- 4.0.0
- SciKit-Learn Version - 1.3.0
Using this solution, we are able to use LightGBM in Python with simple steps. This process also facilitates an easy-way-to use, hassle-free method to create a hands-on working version of code which would help us to use LightGBM in Python.
C++ 15042 Version:v3.3.5 License: Permissive (MIT)
1. What is LightGBM, and how does it differ from a Gradient Boosting Decision Tree?
LightGBM is a gradient-boosting framework. Decision trees form the basis of this. This is to increase the efficiency of the model. To reduce memory usage, people use it. It uses two techniques. Gradient-based One Side Sampling (GOSS) and Exclusive Feature Bundling (EFB).
LightGBM has a faster rate of execution. This is along with being able to maintain good accuracy levels. It is due to the utilization of two techniques. The data instances have no native weight in Gradient Boosted Decision Trees. GOSS leverages it.
2. Who are the main learners using LightGBM, and what learning algorithms do they employ?
LightGBM uses decision trees as the base learners. It updates their weights in each iteration based on the gradient descent algorithm.
3. What advantages of decision tree algorithms make them suitable for machine learning models?
- Decision trees need less effort for data preparation during pre-processing than other algorithms.
- A decision tree does not need the normalization of data.
- You can create a decision tree without scaling the data as well.
4. How can I create training and testing sets to test my model's performance with LightGBM?
We change the data into training and testing sets. We have done this using train_test_split(). This is with a test size of 20%. We train a LightGBM model with 1000 estimators. Maximum tree depth is 5. Make predictions on the test set. Finally, we print the confusion matrix.
5. Are there any restrictions or limitations when using LightGBM?
Overfitting: Light GBM splits the tree leaf-wise. It can lead to overfitting. It produces many complex trees.
Compatibility with Datasets: Light GBM is sensitive to overfitting. So, that can easily overfit small data.