It uses gradient boosting - a hard to use technique which can outperform neural networks. Gradient boosting is powerful but it's easy to shoot yourself in the foot using it.
The documentation on how to use it is quite lacking. The only good source of information on how to properly configure a model to yield good results is this video: https://www.youtube.com/watch?v=usdEWSDisS0 . We had to dig around GitHub issues to find out how to make it work with ClickHouse.
CatBoost is fast. Other libraries will take ~5X to ~10X as long to do what CatBoost does.
CatBoost will do preprocessing out of the box (fills nulls, apply standard scaling, encodes strings as numbers).
CatBoost has all functions you'd need (metrics, plotters, feature importance)
It makes sense to split what CatBoost does into 2 parts:
preprocessing (fills nulls, apply standard scaling, encodes strings as numbers)
number crunching (convert preprocessed numbers to another number - ex: revenue of impression)
The number crunching part of Fast.ai is no-config. For CatBoost you need to configure it, a lot.
CatBoost won't simplify or hide any complexity of the process. So you need to know data science terms and what it does (ex: if your model is underfitting you can use a smaller l2_reg parameter in the model constructor).
In the end both Fast.ai and CatBoost can yield comparable results.
Regarding deploying models, CatBoost is really good. The model runs fast, it has a simple binary format which can be loaded in ClickHouse, C, or Python and it will encapsulate pre-processing with the binary file. Deploying Fast.ai models at scale/speed is impossible out of the box (we have our custom solution to do it which is not simple).
TLDR: CatBoost is fast, produces awesome models, is super easy to deploy and it's easy to use/train (after becoming familiar with it despite the bad documentation & if you know data science terms).
The project seems to be a good idea but it's too young. I was using the GUI version and I've encountered some bugs, and none of those bugs have a good error message.
It won't show data in preview.
The "download" button won't work.
It's trying to create and drop tables in ClickHouse without me asking it to.
Other than bugs:
It will only use 1 core to do everything (training, analysis, download).
Analysis will only run with a very small subset of data, if I use something like 1M rows it never finishes.
Training a model on 100k rows took 25 minutes - (CatBoost takes 90s to train with 1M rows)
The model trained on MindsDB is way worse. It had r-squared of 0.46 (CatBoost=0.58)
To me it seems that they are a plugin which connects ClickHouse to MySQL to run the model in Pytorch.
It's too complex and hard to debug and understand. The resulting model is not good enough.
TLDR: Easy to use (if bugs are ignored), too slow to train & produces a bad model.
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