Attributeerror onehotencoder object has no attribute transform Throws AttributeError: 'OneHotEncoder' object has no attribute '_legacy_mode' Versions For classification, I was trying to convert categorical data into numeric by applying OneHotEncoder. toarray() However i get the following error ValueError: could not convert string to float: I was trying to use the ColumnTransformer from the sklearn compose module but had an error when I used the fit_transform ( ) parameter> one_hot = OneHotEncoder () transformer = ColumnTransformer ([("one_hot", . transform(indexer) You are setting data to be equal to the OneHotEncoder() object, not transforming the data. # 3. transform(ray_dataset) File "/mnt/softwares You need to fit it first - before fitting, the attribute does not exist indeed: encoder = OneHotEncoder(inputCol="index", outputCol="encoding") encoder. # 2. But it shows error could not convert string to float. one_hot, . py", line 542, in <module> train_test() File "/mnt1/mytest/test. fit_transform(X). py", line 506, in train_test config, dataset = preprocessing_data(ray_dataset) File "/mnt1/mytest/test. It should look like this. And error message: from sklearn. transform(data) streamlit error in AttributeError: 'OneHotEncoder' object has no attribute '_infrequent_enabled' ‘error’ : Raise an error if an unknown category is present during transform. setDropLast(False) ohe = encoder. Call 'fit' with appropriate arguments before using this method. categorical_features)], remainder = "passthrough") transformed_X= transformer. py", line 408, in preprocessing_data transformed_dataset = one_hot_encoder. FIT. Transform. You need to fit it first - before fitting, the attribute does not exist indeed: encoder = OneHotEncoder(inputCol="index", outputCol="encoding") encoder. INSTANTIATE. transform(indexer) NotFittedError: This OneHotEncoder instance is not fitted yet. Actual Results. Here is the sample of my categorical data set and code of One Hot Encoding. fit_transform (X) Traceback (most recent call last): File "/mnt1/mytest/test. fit(indexer) # indexer is the existing dataframe, see the question indexer = ohe. preprocessing import OneHotEncoder onehotencoder=OneHotEncoder(categorical_features=[10]) Y= onehotencoder. ‘ignore’ : When an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will be all zeros. setDropLast(False) indexer = encoder. transform(ray_dataset) File "/mnt/softwares . In the inverse transform, an unknown category will encoder = OneHotEncoder(inputCol="index", outputCol="encoding") encoder. encoder=OneHotEncoder(inputCol="GenderIndex",outputCol="gendervec") data = encoder. # 1. You need to call a transform to encode the data. lsllkcmj fnxk robhze ufbg vxcue ulkaoef uvb ebhi ogmp itaiqqwj