Feature engineering is a resource-intensive process for machine learning. Data scientists spend a lot of time in this step. It’s a crucial part of the machine learning pipeline. In the feature engineering process, data scientists use their domain knowledge to create explanatory variables or features. With the help of these features, machine learning algorithms can better interpret the data and create more accurate models.
Until now, feature engineering has been a manual process. However, tools like Featuretools, an open-source Python library for feature engineering, is making it possible to automate the process. It’s changing the way machine learning training is conducted. It’ll lead to faster and more efficient machine learning processes.
Feature Engineering: Manual To Automated
Data scientists use collected data to train machine learning algorithms. Inputs with known outputs are fed into the system to create models. But the input data needs to be sensible.
If the input data is haphazard and scattered, then the machine learning models will produce faulty or subpar results. So data scientists construct new features from the raw data to help machine learning algorithms. This process of creating features from datasets is called feature engineering.
However, manually aggregating millions of data points from multiple database tables into a single feature table is tiresome work. It’s also time-consuming and error-prone. So automation seems like the natural next step.
Benefits Of Automated Feature Engineering
Automated feature engineering produces multiple candidate-features from the raw dataset. Data scientists can use the best candidates to train the machine learning models.
Here are some of the main benefits of automated feature engineering:
Reduces Modeling Time – Most of the feature engineering tasks are repetitive. So automation can speed up the modeling process. In some cases, it can result in 10X speed.
Eliminates Errors – Human errors can lead to faulty features. As a result, data scientists have to waste time re-engineering the features. Automation gets rid of the possibility of human errors.
Higher Quality – In manual production, speed is generally associated with the loss of quality. But in automated feature engineering, there is no need to compromise between speed and quality. Due to the elimination of human errors, the final results are generally better. It leads to higher performance in predictive models.
Currently, automated feature engineering produces too many options. Data scientists have to choose the right ones to train their models. But the tools are improving every day. In the future, automated feature engineering tools will create more targeted options. It will further improve the speed and performance of feature engineering processes and make it easier for data scientists to train machine learning models.