Feature Engineering: The Ultimate Guide.

Overview

In this post, I'll talk about feature engineering, which is one of the most crucial phases in creating an outstanding machine learning model.

Key Topics Covered.

• What is feature engineering?
• Importance of feature engineering.

• Processes Involved in Feature Engineering

• Feature engineering techniques for machine learning.

• Best tools for feature engineering.

What is feature engineering?

Feature engineering is the act of turning raw data into meaningful information that machine learning algorithms can exploit. To put it another way, it's the process of identifying, removing, and modifying the most pertinent features from the accessible data in order to create machine learning models that are more precise and effective.

Regarding machine learning a feature is a single, quantifiable quality or characteristic of a data point that is used as inputs for a machine learning algorithm. The caliber of the features used to train machine learning models has a major impact on their performance.

• For example, Features in a consumer demographic dataset may include age, gender, occupation, and income level.

Most of the time, the data that is dealt with when creating machine learning models appears to be disorderly chaos. Feature engineering is applied when this data needs to be cleaned up and pre-processed in order to be used. The goal of this procedure is to bring order to chaos.

Importance of feature engineering.

Making your data more appropriate for the task at hand is the basic aim of feature engineering. Other reasons for engineering features include:

• Improve User Experience: We can enhance the product's usability, effectiveness, and intuitiveness by including additional features.

• Competitive Advantage: We can set our products apart from the competition and draw in more clients by providing distinctive and cutting-edge features.

• Meet Customer Needs: We can determine areas where new features could improve the product's value and satisfy consumer needs by examining user feedback, market developments, and consumer habits.

• Increase Revenue: It is also possible to develop features to increase income.

• Future-Proofing: We can create features that guarantee the product stays relevant and helpful over time by foreseeing future trends and possible client needs.

Processes Involved in Feature Engineering

Feature engineering techniques for machine learning.

In feature engineering, a variety of methods can be applied to combine or modify existing features to produce new ones. Some of the often employed feature engineering methods are as follows:

• Scaling : One of the most prevalent and complex issues in machine learning is feature scaling, but it's also one of the most crucial issues to do right. We require data with a known set of features that must be scaled up or down as necessary in order to train a predictive model.
  The two most used scaling strategies are normalization and standardization. The variable is scaled via standardization to have a zero mean and a unit variance. The variable is scaled via normalization to have a range of values between 0 and 1.

• One-Hot Encoding : One method for converting categorical data into numerical values that machine learning models can use is called one-hot encoding. Using this method, every category is converted into a binary value that indicates whether it exists or not.

• Feature Split : Feature splitting is an effective feature engineering technique that divides a single feature into several sub-features or groups according to set criteria. This procedure improves the model's capacity to identify intricate linkages and patterns in the data while also revealing insightful new information.

• Handling Outliers : A strategy for eliminating outliers from a data set is called outlier treatment. On many different scales, this technique can be applied to generate a more accurate data representation. Model training shouldn't begin until this process is finished.

• Binning : Continuous variables can be converted into categorical variables using the binning approach. The continuous variable's range of values is split into many bins using this technique, and a category value is assigned to each bin. Take "Age," a continuous variable with values ranging from 18 to 50, as an example. In order to assign a category value to each age group, Binning would divide this variable into many age groups, such as 18–25, 26–35, and 36–50.

Best tools for feature engineering.

There are numerous feature engineering tools at one's disposal. These are a few well-known ones.

FeatureTools

A Python package called featuretools makes it possible to automatically create features for structured data. It may create new features based on user-defined primitives and retrieve features from a variety of tables, including relational databases and CSV files.

ExploreKit

ExploreKit finds common operators so you may combine several features or change each one separately.

AutoFeat

Using automated feature engineering and selection, AutoFeat facilitates the execution of linear prediction models. You can choose the units of the input variables with AutoFeat to prevent features from being constructed that make no sense physically.

TsFresh

Python has a module called TsFresh. Numerous time series characteristics or attributes are automatically calculated by it. The package contains techniques for evaluating such features relevance and explanatory power in regression and classification tasks. Things like the quantity of peaks, average value, maximum value, and time reversal symmetry statistic can all be extracted with its assistance. FeatureTools integration is possible with it.

DataRobot

DataRobot creates new features and chooses the ideal feature-model combination for a given dataset using automated machine learning techniques.

H2O.ai

A variety of automatic feature engineering techniques, including feature scaling, imputation, and encoding, are available on the open-source machine learning platform H2O.ai. For more experienced users, manual feature engineering capabilities are also available.

Final Thoughts

When done correctly, feature engineering is one of the most valuable techniques of data science, but it is also one of the most challenging.