Catboost Vs LightGBM – Comparision to Find the Best

Which one is better, Catboost vs LightGBM? Many people are confused about which one to use when it comes to getting fast and accurate results. A Catboost is an algorithm that was developed by Yandex which is an online taxi company. Probably one of the biggest companies in Russia. While the LightGBM was developed by the Microsoft company and was made publically available after 2017. Both of them are super fast boosting algorithms and here we will be discussing the features of both algorithms. It is up to you and your dataset to decide which one you will choose for your dataset among Catboost vs LightGBM.

CatBoost Vs LightGBM

Catboost is a gradient-boosting algorithm that can be used for both regression and classification problems. The model is especially very accurate when we have a large number of categorical values in our dataset. It handles categorical values by its own unique method. In CatBoost, the cat represents the categorical values. So, if you have a dataset that has more categorical values, then we will recommend you use the CatBoost algorithm.

Catboost is not a Python built-in module, so you need to install the model on your system before using it. You can use the pip command to install the Catboost shown below:

# install catboost
pip install catboost

Once the model is installed, you can import it to use in your Python Script as shown below:

# importing the model
import catboost

Now, you can use the Catboost model and all its functionalities.

On the other hand, LightGBM is also a Gradient Boosting algorithm which means it creates small weak learners and combines them to create a strong predictive model. The LightGBM can also be used to predict classification and regression values. Similar to Catboost, it is also not a Python built-in module, so we need to install the LightGBM before using it on our system.

Use the pip command to install the module on your system.

# install lightgbm
pip install lightgbm

Once the installation is complete, you can then import the module to use it in your Python script.

import lightgbm

Run the file and if you didn’t get any error, it means the module was installed successfully.

Features of CatBoost Algorithms

Catboost is a fast, accurate, and really cool algorithm that is getting popular day by day. Here we will list some of its awesome features and it is up to you to use it or not.

• It handles categorical values with a unique approach. So, you don’t need to handle categorical values in the preprocessing steps.
• Catboost has a unique way of encoding
• It has a built-in feature importance so you don’t really need to care about it in a preprocessing step.
• Fast training process. Even if you have a large dataset, it will not take too much time to train.
• Catboost uses “gradient-based one-side sampling” which handles the outliers very effectively.
• The most important feature is the early stop which reduces the risk of overfitting the model.
• It supports GPU.
• It helps automatically in calculating the Shapley values.
• It has a custom loss function
• It supports multiple output class
• Accurate results.
• Handle null values
• And many more.

Features of LightGBM Algorithm

Here we go with the features of LightGBM:

• Gradient boosting algorithm
• It is light and efficient.
• It handles categorical values by its own method.
• It has GPU acceleration
• It has a unique feature known as leaf-wise tree growth.
• It supports exclusive feature-handling approaches.
• Another amazing feature of the LightGBM is the histogram-based gradient boosting method.
• It also supports early stops.
• It has a cross-validation method.
• It contains some regularization techniques.
• It also has an automatic feature importance method.
• Custom loss function
• Handle null value
• And many more.

Final Thoughts

It is really hard to say which algorithm between, Catboost Vs LightGBM is better. Well, to be honest, it all depends on your dataset. There might be cases when the LightGBM will perform better than Catboost and there might be cases when Catboost will perform better. So, depending on your dataset and the features of each of the algorithms, you can decide which one to use.