Does smote work with categorical data?

Does smote work with categorical data?

So as per documentation SMOTE doesn’t support Categorical data in Python yet, and provides continuous outputs. You can instead employ a workaround where you convert the categorical variables to integers and use SMOTE.

Is smote for binary classification?

While Tomek Links and ENN techniques can be applied for multiclass problems but SMOTE can be applied only for binary classification problems.

What is smote NC?

SMOTE-NC is a great tool to generate synthetic data to oversample a minority target class in an imbalanced dataset.

How do you use smote to balance data?

The steps of SMOTE algorithm is:

1. Identify the minority class vector.
2. Decide the number of nearest numbers (k), to consider.
3. Compute a line between the minority data points and any of its neighbors and place a synthetic point.
4. Repeat step 3 for all minority data points and their k neighbors, till the data is balanced.

When to use smote-NC for mixed data?

For example, in the churn data above, we had ‘IsActiveMember’ categorical feature with the data either 0 or 1. If we oversampled this data with SMOTE, we could end up with oversampled data such as 0.67 or 0.5, which does not make sense at all. This is why we need to use SMOTE-NC when we have cases of mixed data.

Do you need smote-NC after or after encoding?

Also, if I use some encoder (BinaryEncoder, one hot, etc.) for categorical data, do I need to use SMOTE-NC after encoding, or before? I copied my example code ( x and y is after cleaning, include BinaryEncoder).

How to smote binary and categorical data in Python?

As per the documentation, this is now possible with the use of SMOTENC. SMOTE-NC is capable of handling a mix of categorical and continuous features. from imblearn.over_sampling import SMOTENC smote_nc = SMOTENC (categorical_features= [0, 2], random_state=0) X_resampled, y_resampled = smote_nc.fit_resample (X, y)

Which is better smote or smote-NC based on AUC?

SMOTE-NC with the Adult dataset differs from our typical result: it performs worse than plain under-sampling based on AUC. […] even SMOTE with only continuous features applied to the Adult dataset, does not achieve any better performance than plain under-sampling.