I have a dataset with some numerical and categorical features and I am trying to apply CatBoost for categorical encoding and classification.

Since my dataset is highly imbalanced, with a large number of data samples with label 0 compared to those with label 1, I'm also trying to use SMOTE to synthesize label 1 data samples before CatBoost classification.

My code -

# train_categorical_features is a list of columns that have categorical values
train_pool = Pool(data = X,
                  label = y,
                  cat_features = train_categorical_cols)

X_enc = train_pool.get_features()
y_enc = train_pool.get_label()

smote = SMOTE()
X_res, y_res = smote.fit_resample(X_enc, y_enc)
print('Resampled dataset samples per class {}'.format(Counter(y_res)))

predictions = []
for i in range(10):
    clf = CatBoostClassifier(learning_rate=0.08,
                         depth = 10,
                         l2_leaf_reg = 4,
    clf.fit(train_pool, plot=True,silent=True)

I get an error on X_enc = train_pool.get_features() that says -

CatBoostError: Pool has non-numeric features, get_features supports only numeric features

My questions are -

  1. Is my approach towards applying SMOTE with CatBoost correct?
  2. I've diligently followed the catboost documentation, and I am not able to understand or fix the error I've mentioned above. Would appreciate any help.

1 Answer 1


The reason you are getting the error is that your pool contains categorical features. If all of your features were numerical, it would work fine.

SMOTE is irrelevant here.

  • $\begingroup$ Hi! Thank you for your answer! Quick clarification - is categorical encoding happening when I fit the data into the model? i.e., after clf.fit(train_pool...) $\endgroup$ Commented Feb 5, 2020 at 14:26
  • $\begingroup$ @AishwaryaAR, Yes, I am pretty sure that it is the case. Also categorical encoding happens per split. You can find more info here: catboost.ai/docs/concepts/… $\endgroup$
    – Akavall
    Commented Feb 5, 2020 at 18:05

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