I'm trying to implement One Hot Encoding on selected Categorical columns
mask = df.columns.isin(['cp','restecg','slope','thal'])[:-1]
After applying One Hot Encoding like this:
enc = OneHotEncoder(categories=mask, sparse=False)
and instantiate fit like this:
enc.fit(X_train)
I'm getting this error
axis -1 is out of bounds for array of dimension 0
So I searched many places, to what I'm doing wrong, some mentioned that calling OneHotEncoding directly will not work, so I tried this:
from sklearn.compose import ColumnTransformer
enc = ColumnTransformer([(mask, OneHotEncoder())],
sparse_threshold=False)
After this, I tried to fit in again like this:
enc.fit(X_train)
Now, I'm getting this error:
ValueError: not enough values to unpack (expected 3, got 2)
What am I doing wrong?