Usually when you think of a gradient boosted decision tree you think of XGBoost or LightGBM. I'd heard of CatBoost but I'd never tried it and it didn't seem too popular. I was looking at a Kaggle competition which had a lot of categorical data and I had squeezed just about every drop of performance I could out of LGBM so I decided to give CatBoost a try. I was extremely impressed.

Out of the box, with all default parameters, CatBoost scored better than the LGBM I had spent about a week tuning. CatBoost trained significantly slower than LGBM, but it will run on a GPU and doing so makes it train just slightly slower than the LGBM. Unlike XGBoost it can handle categorical data, which is nice because in this case we have far too many categories to do one-hot encoding. I've read the documentation several times but I am still unclear as to how exactly it encodes the categorical data, but whatever it does works very well.

I am just beginning to try to tune the hyperparameters so it is unclear how much (if any) extra performance I'll be able to squeeze out of it, but I am very, very impressed with CatBoost and I highly recommend it for any datasets which contain categorical data. Thank you Yandex! 

Etiketten: coding, data_science, machine_learning, kaggle, catboost
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I recently started looking at a Kaggle Challenge about predicting poverty levels in Costa Rica. I used sklearn train_test_split to split the training data into train and validation sets and fit a few models. The first thing I noticed was that my submissions scored significantly lower than my validation sets: 0.36 on the submission vs. .96 on my validation data.

The data consists of information about individuals with the target as their poverty level. The features include both information relating to that individual as well as information for the household they live in. The data includes multiple individuals from the same household, and some exploratory data analysis indicated that most of the features were on a household level rather than the individual level.

This means that doing a random split ends up including data from the same household in both the train and validation datasets, which will result in the leakage that artificially raised my initial validation scores. This also means that my models were all tuned on a validation dataset which was essentially useless.

To fix this I did the split on unique household IDs, so no household would be included in both datasets. After re-tuning the models appropriately, the validation f1 scores had gone down from 0.96 to 0.65. The submissions scores went up to 0.41, which was not a huge increase, but it was much closer to the validation scores.

The moral of this story is never forget to make sure that your training and validation sets don't contain overlap or leakage, or the validation set becomes useless.

Etiketten: data_science, machine_learning, kaggle
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