I am reading everywhere on new questions and blogs that since version 0.20, OneHotEncoder is able to handle string features.
Moreover, the documentation is what looks more ambiguous. Here are the first two lines from the documentation:
Encode categorical integer features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features.
First line says it
encodes categorical integer features
and the next line says
input should be array like of integers or strings.
When i tried it, i still got the value error.
print(X.columns) encoder = OneHotEncoder(categorical_features=[1,4,5]) encoder.fit(X) Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region'], dtype='object') ValueError: could not convert string to float: 'female'
I am aware of the means to handle encoding of string features with
pd.getDummies() but specifically want to understand about this.