Feature engineering automation

February 9, 2023

Machine learning is a very fast-growing IT industry. Feature engineering (FE) is a vital step in machine learning data preparation. Carefully chosen features can improve ML modeling efficiency and provide more accurate results for the whole model performance. FE involves extracting meaningful traits from the raw data, sorting the figures, removing repetitive entries, and transforming the data into more valuable insights via data normalization, transformation, scaling, etc. Read the detailed explanation of feature engineering techniques here.

Feature tools

This is one of the most popular libraries for computer-aided feature design. It supports many features, including:

feature selection,
Feature construction,
using relational databases to create new features, etc.

It also provides many primitives that constitute primary transformations usage, max, sum, mode, and so on. These are helpful operations. Say you need to find the average time between events from a log file and can use primitives to do that.

But one of the most important aspects of feature tools is that it uses profound feature synthesis (DFS) to build features.

Let's understand what DFS is. This algorithm needs entities. Imagine that entities are several interconnected data tables. It then stacks the primitives and performs column transformations.

These operations can mimic the kinds of transformations humans perform.

Here is one great library for creating baselines; it can mimic what people do by hand.

Autofeat

Autofeat is another open-source library for feature engineering. It automates feature synthesis, feature selection, and linear machine-learning model fitting.

Autofeat's algorithm is quite simple. It generates nonlinear features, such as log(x), x2, or x3. It uses different operands, such as negative, positive, and decimal numbers, in creating the feature space. This leads to an exponential growth of the feature space. Categorical traits are changed into the one-point encoded feature.

We need to select the significant attribute now that we have many features. Outset Autofeat removes highly correlated traits today. It uses L1 regularization and removes quality with low coefficients. This process of appointing correlated traits and removing characteristics using L1-regularization is recurrent several times until only a few traits remain. These features are selected using an iterative process that describes the dataset.

Tsfresh

Tsfresh is an open-source Python package for creating temporary strings and sequential functions. In the packet, we can create thousands of rules using multiple wires. In addition, the pack uses the Scikit Learn method, which allows us to include the bundle in the pipeline.

Feature engineering from Tsfresh is different because the extracted features cannot be used directly in machine learning model training. These functions were used to describe a time series dataset and require additional steps to incorporate the data into the training model.

PyCaret

PyCaret is an open-source Python machine-learning library that automates machine-learning workflows. It is a collaborative machine learning and model control tool that intuitively speeds up experimentation, making you more productive.

Unlike other open-source machine learning libraries, PiCaret is an alternative low-code library that you can use to replace hundreds of lines of code in just a few lines.

Unlike the other frameworks in this paper, PyCaret is not a specialized library for automated feature engineering but contains functions to automatically generate features for the model.

Conclusion

There are many more tools for feature automation, but the best result is the smart composition of the manual feature selection strategy and professional automation of furter work, like normalization, scaling, etc. Think ahead about the best strategy for your model, and then you can gain all the benefits from the feature automation. If you need a piece of expert advice on ML project, do not hesitate to request a consultation here.