# Binary Classification - One Hot Encoding preventing me using Test Set [duplicate]

I have a preprocessing pipeline that includes replacing missing values and onehotencoding for the categorical variables.

When I try to use my model on the test set, it explains that the number of columns it expects differs. This is due to one hot encoding

One option I considered was passing the full dataset into the pipeline and then seperating into test and split. However, this causes data leakage as the missing values it capturing values from the testset.

Please let me know how to prevent this.

Thanks,

• Can you share the error message and what your data looks like? Nov 24, 2019 at 10:56
• I can't share what the data looks like since its client data, however I can share the error message. I have just left the house and will add it when I'm back in the evening. Sorry for not adding it initially Nov 24, 2019 at 10:57

You can use handle_unknown parameter of sklearn while encoding training data.

sklearn.preprocessing.OneHotEncoder(handle_unknown='ignore')


When this parameter is set to ‘ignore’ and an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will be all zeros.

Note : I assumed you are using scikit-learn.

Source : sklearn.preprocessing

You need to apply one-hot-encoding before you split your data. Otherwise you will run into problems if there is a categorical attribute whose values are not all present in the train and test data.

It is a bit of guessing since I do not know what your data looks like but it might be what happened in your case. Here is a simple example. Suppose you have the following data sets obtained from your split before one-hot-encoding:

Train data:
attribute_1
1        a
2        b

Test data:
attribute_1
1        a
2        b
3        c


If you apply one-hot-encoding to these data sets separately you will end up with the following:

Train data:
attribute_1_a     attribute_1_b
1        1                   0
2        0                   1

Test data:
attribute_1_a     attribute_1_b     attribute_1_c
1        1                   0                 0
2        0                   1                 0
3        0                   0                 1


As you can see the columns of your train and test data do not match anymore. This can be solved by one-hot-encoding before splitting into train and test data.

And for the one-hot-encoding I do not see any problems with data leakage.

Alternatively, e.g. if you have missing data which you want to impute before one-hot-encoding, you can split the data first and then "manually" make sure that both datasets have the same attrributes.

For example like this:

# create example dataframes
df_train = pd.DataFrame({
"attribute_1_a": [1, 0],
"attribute_1_b": [0, 1]
})

df_test = pd.DataFrame({
"attribute_1_a": [1, 0, 0],
"attribute_1_b": [0, 1, 0],
"attribute_1_c": [0, 0, 1]
})

# add missing columns to test dataset with all values being 0
for i in df_train.columns:
if i not in df_test.columns: df_test[i] = 0

# add missing columns to train dataset with all values being 0
for i in df_test.columns:
if i not in df_train.columns: df_train[i] = 0

# use the same column order for the test set as for train
df_test = df_test.reindex(df_train.columns, axis=1)



Now the dataframes will look like this and have the same attributes:

In: df_train

Out:
attribute_1_a  attribute_1_b  attribute_1_c
0              1              0              0
1              0              1              0

In: df_test

Out:
attribute_1_a  attribute_1_b  attribute_1_c
0              1              0              0
1              0              1              0
2              0              0              1


However, check your datasets after this manipulation to make sure it went thru properly and you do not have any inconsistencies!

• Thank you for your answer.. So I've done that solution, however one hot encoder won't work with missing values. So by using an Imputer ild be capturing information from the test set. I geuss its the tradeoff I may have to consider. Nov 24, 2019 at 13:18
• @VirajVaitha I have added a way to one-hot-encode after the split Nov 25, 2019 at 10:59