What do you think, which one is better among Catboost vs XGBoost? Well, it is hard to tell which one is better than the other because it totally depends on their usage. In some cases, the Catboost might give good results while on the other hand, in some cases the XGboost might give better results. So, it totally depends on the kind of dataset and the background. Here, we will go through some of the features of both algorithms which will help you to decide which one to use on your dataset.
Catboost Vs XGboost
Catboost is an open-source boosting algorithm that is specifically built for categorical values. The cat in Catboost stands for categorical values. It handles the categorical values with its own unique formatting. When using the Catboost, we don’t need to be worried about the encoding process as it is also done automatically by the algorithms. Similarly, the algorithm also handles the null values. Using Catboost for the predictive model means spending less time on preprocessing of the dataset which is one of the most important parts.
Learn the Hyperparameter tuning of Catboost.
Before using the algorithm, you need to install the module on your system. The most common way of installation is the pip command.
# install python module pip install catboost
It can take some time to install the module as it is a heavy module. Once the installation is complete, import the module.
# importing the module import catboost
Run the file and if it does not return an error, it means the installation was successful.
On the other hand, XGboost is also a boosting algorithm. It creates various weak learners which helps to predict the output class and try to reduce the error made by the previous weak models. It provides various important features. It is fast and gives results with high accuracy. It also handles null values automatically. Similar to Catboost, we have to install the XGBoost on our system before using it.
# install XGboost pip install xgboost
Once the installation is complete, you can then import the XGboost module.
# importing module import xgboost
To answer the question of which one is better Catboost vs Xgboost, it is hard to decide like this. Both algorithms have their own features and both are good for specific datasets. So, depending on your dataset, you can choose which one is suitable for you.
Important Features of Catboost
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 use “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.
Important Features of the XGboost Algorithm
XGboost also provides some great features which can be accessed by installing and importing the module to your Python script. Here, we will list some of the important features.
- It is a gradient-boosting algorithm.
- Regularization is another important feature of the algorithm.
- One of the features that make the XGBoost different from Catboost is the tree pruning feature.
- It handles missing values.
- It has an automatic cross-validation method.
- Feature importance.
- To avoid overfitting the model, the XGboost algorithm supports early stops.
- GPU acceleration
- And many more.
The debate of Catboost vs. XGboost and other boosting algorithms will continue forever. It is hard to decide which one is better than the others because the performance usually depends on the kind of dataset you are using. So, it is up to you to decide which one to use based on your dataset.