# Machine Learning with CatBoost

Recently, during a hackathon event with my colleagues Iakov Davydov and Rudolf Biczok, I learned CatBoost, an open-source library of gradient boosting on decision trees, which is particular friendly with categorical features. Users do not need to pre-process categorical features and can directly start inference, which the authors argue is both fast and accurate.

I would like to thank the authors at the Russian company Yandex for sharing this piece of software open-source. Here I shortly summarize my impressions of the method.

# Gradient boosting

Gradient boosting can be used for both regression and classification problems. It produces a prediction model in the form of an *ensemble* of *weak* prediction models, typically decision trees.

In general, boost methods work by iteratively learning weak classifiers and adding them to a final, strong classifier. After a weak learner is added, the data are re-weighted so that wrongly classified examples gain weight and correctly classified examples lose weight. In this way, future weak learners are trained to concentrate more on the misclassified samples.

In the case of regression, during the training process, each model is fitted to the residual between target variable \(y\) and the output of existing model ensemble \(F_{m}(x)\), or \(y - F_{m}(x)\). Note that this happens to be the negative gradients with respect to \(F(x)\) of the squared error loss function \(\frac{1}{2}(y-F(x))^2\). Therefore gradient boosting is a gradient descent algorithm.

Other algorithms in this class include AdaBoost and XGBoost.

# Hand-on of CatBoost

The CatBoost website provides a comprehensive tutorial introducing both python and R packages implementing the CatBoost algorithm. A jupyter notebook is available to explore some base cases of using CatBoost.

The python package can be installed via `pip`

. And below is a minimal example to test that the CatBoost installation works.

```
import numpy as np
from catboost import CatBoostRegressor
dataset = np.array([[1,4,5,6], [4,5,6,7], [30,40,50,60], [20,15,85,60]])
train_labels = [1.2, 3.4, 9.5, 24.5]
model = CatBoostRegressor(learning_rate=1, depth=6, loss_function='RMSE')
fit_model = model.fit(dataset, train_labels)
print(fit_model.get_params())
```

# Why CatBoost has superior performances

According to the key reference on arXiv, CatBoost: unbiased boosting with categorical features, two critical algorithmic advances were introduced in CatBoost.

The first is the implementation of *ordered boosting*, a permutation-driven alternative to the classic algorithm. The second is a new way to treat categorical features. Both techniques were created to avoid the prediction shift that is present in currently existing implementations of gradient boosting algorithms.

I need more time to dig into the details. Apparently the authors spent a lot of time benchmarking CatBoost against other gradient-boosting algorithms, and therefore this article is at the same time a very good introduction to this class of algorithms.

# Impressions and conclusions

CatBoost seems very well equipped for real-world machine learning problems where a large number of categorical variables need to be considered. It is fast and accurate based on my experience. When the data is not well standardized and the model training time is limited, I think CatBoost is likely a better choice than methods that rely on heavy training and parameter/structure tuning, such as SVM and DNN. I will explore the method more to understand how exactly it ticks.