catboost VS Prophet

CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.

Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.

Catboost - CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. Link - https://catboost.ai/

CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs.

If you considering GBDT check out catboost, unfortunately RF mode is not available but library implement lots of interesting categorical encoding tricks that boost accuracy.

The data contains a large number of categorical features. The most suitable for obtaining a base-line model, in my opinion, is CatBoost. It is a high-performance, open-source library for gradient boosting on decision trees.

https://facebook.github.io/prophet/

"Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well."