I'm trying to use the data visualization feature in catboost - the one that visualizes training vs validation error (learning curve): model.fit(X_train, y_train
This is my data. I created a model with CatBoostClassifier(). I can get the feature names list with: >>> model.feature_names_ ['title', 'value'] Firs
I have a list of models that I iterate through in a for loop getting their performances. I've added catboost to my model list, but when I try to add it's best e
I have built a machine learning model using Catboost classifier to predict the categoryname of my result as per below screenshot1. However, if I get an unknown
This is a pretty dumb question, but I couldn't find anywhere, so I will take my chances in here... I'm building a classifier using CatBoost. Since this is a NLP
I'm training catboost on a dataset made of 41k observations and ~60 features. The dataset is a longitudinal series (9 years) that is spatially distributed. At t
is there a way to have pruning with CatBoost and Optuna (in LightGBM it's easy but in Catboost I can't find any hint). My code is like this def objective(trial)
I want to use default hyperparams in randomized search, how can I do it? (per_float_feature_quantization param here) grid = {'learning_rate': [0.1, 0.16, 0.2],
I have this pre-trained saved model, where I informed my categorical features, and it's working fine if I predict right after training. Now I wanna use it again
I use a CatBoostClassifier and my classes are highly imbalanced. I applied a scale_pos_weight parameter to account for that. While training with an evaluation d
In the introduction/promo video (https://www.youtube.com/watch?v=s8Q_orF4tcI) you have mentioned that Catboost can analyse the time series historical data for w