The way in which we construct conventional machine studying fashions is to first practice the fashions on a “coaching dataset” — usually a dataset of historic values — after which later we generate predictions on a brand new dataset, the “inference dataset.” If the columns of the coaching dataset and the inference dataset don’t match, your machine studying algorithm will normally fail. That is primarily as a consequence of both lacking or new issue ranges within the inference dataset.

The primary drawback: Lacking elements

For the next examples, assume that you simply used the dataset above to coach your machine studying mannequin. You one-hot encoded the dataset into dummy variables, and your totally remodeled coaching information appears like under:

Now, let’s introduce the inference dataset, that is what you’ll use for making predictions. Let’s say it’s given like under:

# Creating the inference_data DataFrame in Python
inference_data = pd.DataFrame({
'numerical_1': [11, 12, 13, 14, 15, 16, 17, 18],
'color_1_': ['black', 'blue', 'black', 'green', 
'green', 'black', 'black', 'blue'],
'color_2_': ['orange', 'orange', 'black', 'orange', 
'black', 'orange', 'orange', 'orange']
})

Utilizing a naive one-hot encoding technique like we used above (pd.get_dummies)

# Changing categorical columns in inference_data to 
# Dummy variables with integers
inference_data_dummies = pd.get_dummies(inference_data, 
columns=['color_1_', 'color_2_']).astype(int)

This is able to rework your inference dataset in the identical means, and also you receive the dataset under:

Do you discover the issues? The primary drawback is that the inference dataset is lacking the columns:

missing_colmns =['color_1__red', 'color_2__pink', 
'color_2__blue', 'color_2__purple']

For those who ran this in a mannequin skilled with the “coaching dataset” it could normally crash.

The second drawback: New elements

The opposite drawback that may happen with one-hot encoding is that if your inference dataset contains new and unseen elements. Think about once more the identical datasets as above. For those who study intently, you see that the inference dataset now has a brand new column: color_2__orange.

That is the alternative drawback as beforehand, and our inference dataset comprises new columns which our coaching dataset didn’t have. That is really a standard prevalence and might occur if one in all your issue variables had modifications. For instance, if the colors above signify colors of a automotive, and a automotive producer instantly began making orange vehicles, then this information won’t be obtainable within the coaching information, however may nonetheless present up within the inference information. On this case you want a sturdy means of coping with the difficulty.

One may argue, properly why don’t you record all of the columns within the remodeled coaching dataset as columns that may be wanted to your inference dataset? The issue right here is that you simply typically don’t know what issue ranges are within the coaching information upfront.

For instance, new ranges might be launched commonly, which may make it tough to take care of. On high of that comes the method of then matching your inference dataset with the coaching information, so that you would want to examine all precise remodeled column names that went into the coaching algorithm, after which match them with the remodeled inference dataset. If any columns have been lacking you would want to insert new columns with 0 values and if you happen to had further columns, just like the color_2__orange columns above, these would have to be deleted. It is a reasonably cumbersome means of fixing the difficulty, and fortunately there are higher choices obtainable.

The answer to this drawback is reasonably simple, nonetheless lots of the packages and libraries that try to streamline the method of making prediction fashions fail to implement it properly. The important thing lies in having a perform or class that’s first fitted on the coaching information, after which use that very same occasion of the perform or class to remodel each the coaching dataset and the inference dataset. Beneath we discover how that is performed utilizing each Python and R.

In Python

Python is arguably one the most effective programming language to make use of for machine studying, largely as a consequence of its intensive community of builders and mature package deal libraries, and its ease of use, which promotes speedy improvement.

Concerning the problems associated to one-hot encoding we described above, they are often mitigated through the use of the broadly obtainable and examined scikit-learn library, and extra particularly the sklearn.preprocessing.OneHotEncoder class. So, let’s see how we will use that on our coaching and inference datasets to create a sturdy one-hot encoding.

from sklearn.preprocessing import OneHotEncoder
# Initialize the encoder
enc = OneHotEncoder(handle_unknown='ignore')
# Outline columns to remodel
trans_columns = ['color_1_', 'color_2_']
# Match and rework the information
enc_data = enc.fit_transform(training_data[trans_columns])
# Get characteristic names
feature_names = enc.get_feature_names_out(trans_columns)
# Convert to DataFrame
enc_df = pd.DataFrame(enc_data.toarray(), 
columns=feature_names)
# Concatenate with the numerical information
final_df = pd.concat([training_data[['numerical_1']], 
enc_df], axis=1)

This produces a ultimate DataFrameof remodeled values as proven under:

If we break down the code above, we see that step one is to initialize the an occasion of the encoder class. We use the choice handle_unknown='ignore' in order that we keep away from points with unknow values for the columns once we use the encoder to remodel on our inference dataset.

After that, we mix a match and rework motion into one step with the fit_transform methodology. And eventually, we create a brand new information body from the encoded information and concatenate it with the remainder of the unique dataset.