So you’ve built a feature store … what next?
Now you have a feature store with hundreds (or thousands!) of columns - you’ll likely need to prep them for inputs to train an ML model.
Here’s the places I found useful when learning how to do this:
So far, I’ve mainly been using the StandardScaler from sklearn along with the one hot encoding using pandas get_dummies.
import pandas as pd my_one_hot_category = pd.get_dummies(my_dataframe['my_category']) my_dataframe.drop('my_category', axis=1, inpace=True)
This is because most of the categorical data I’ve used upto now is easily binned and can then be one hot encoded - without creating a large sparse vector to represent the category.
Embedding, feature hashing, and feature crossing are next on the list to try out.
Once you’ve trained your predictive model and carefully saved your model and its weights - you must also remember to ensure you can repeat any other feature preparation you’ve done. In particular, make sure you’ve saved away your Scaler - so you can use it in your prediction deployment.
It took me a little time to figure out how to save a Scaler object - here’s how I’m doing it - hope it’s right!
from sklearn.preprocessing import StandardScaler scaler = StandardScaler().fit(X) from sklearn.externals import joblib joblib.dump(scaler, "scaler.save") # And then load with: scaler = joblib.load("scaler.save")
Lessons learned the hard way
Here’s a few mistakes made along the way - I made them so you don’t have to!
fillna(0) - don’t be too trigger-happy with replacing nulls with 0. Replacing with the column mean will often make more sense … blindly filling missing data with zero can skew your model.
Know Your Features - after being super-stoked with a high-accuracy, high-precision, and high-recall trained model - I subsequently found that I’d left in a column which was a dependent variable :( Always be suspicious of good results!
RandomizedSearchCV() - is awesome, make sure you know how to use it. You can use this method to optimise accuracy, precision, or recall through k-fold cross validation - then test the best parameters on the held-back test set.
Less is more - sometimes, your model will generalise better with less training epochs, ie, less likelihood over overfitting to the training data.