Overview

Creating new features from preexisting data to aid in more accurate predictions is referred to as feature creation. Calculated features, binning, splitting, and one-hot encoding are a few examples of feature creation. Depending on the kind of data being utilized and the problem being solved, machine learning can make use of a wide range of features.

• Features with numerical values: These include height, weight, and age.
• Categorical features: Discrete values like race, gender, or occupation.
• Textual data: Descriptions, comments, and reviews.
• Image features: Visual components, like colors, borders, and pixels.
• Time-series features: Data that vary over time, like stock prices, weather reports, or sensor readings.
• Audio data: Features related to sound, such as frequency, loudness, or duration.

What is Feature Creation?

Features are variables—observable, quantifiable phenomena that can be noted down. It’s important to choose distinct traits and occasionally create new ones by combining preexisting ones. Consider a Machine Learning application that aims to estimate the likelihood that a patient will develop heart disease. What characteristics might there be?

• Age
• Gender
• Height
• Weight
• Blood pressure
• Heart rate at rest
• Prior medical history

Let’s take a closer look at a few of the features. Organizing features with several values into fewer categories is something we wish to accomplish occasionally. The range of values that are acceptable for age, blood pressure, and resting heart rate is from 0 to an integer with an upper bound. However, do we want 120 possible age values? Why not divide the ages into groups of 0 to 18, 19 to 29, 30 to 39, and so on, to make things easier? Similarly, classify the vital signs into three to seven groups. In terms of calculating data, height and weight can be merged to create BMI, a common feature. Determine your BMI and divide it into a limited number of groups. It is possible to create several binary features from past medical history. Has the patient had a cerebrovascular accident? In short, yes or no. Has high blood pressure been identified as the patient’s condition? Although it’s just the beginning, it provides a solid foundation for recognizing, categorizing, and obtaining valuable features for machine learning applications.

Types of Feature Creation

Domain-Specific

Developing new features based on domain expertise; for example, developing features following industry norms or company regulations.

Data-driven

Developing new features, like aggregations or interaction features, by looking for trends in the data.

Synthetic

Creating new features through the synthesis of fresh data points or the combination of preexisting features.

Importance of Feature Creation

• Enhances Model Performance: Feature generation can boost the accuracy and precision of the model by giving it more and more pertinent data.
• Boosts Model Robustness: By using more characteristics, the model can withstand outliers and other anomalies better.
• Enhances Model Interpretability: By adding new features, the model’s predictions may be simpler to comprehend.
• Increases Model Flexibility: The model’s ability to handle various data types can be increased by incorporating new features.

Advantages of Feature Creation

• Creating new characteristics through the combination of preexisting ones to record correlations between factors.
• Determining which variables are best to include in a prediction model.
• Offering a lot of flexibility by combining existing characteristics to create new ones through addition, subtraction, and ratio.
• Developing new variables that will be most beneficial to our model.
• Enhancing the machine learning model’s overall quality by improving feature engineering.

Why Feature Creation?

• Enhances Model Performance: Feature creation can boost the accuracy and precision of the model by giving it more and more pertinent data.
• Boosts Model Robustness: By using more characteristics, the model can withstand outliers and other anomalies better.
• Enhances Model Interpretability: By adding new features, the model’s predictions may be simpler to comprehend.
• Increases Model Flexibility: The model’s ability to handle various data types can be increased by incorporating new features.

Conclusion

Feature creation is crucial to machine learning, as was covered in the blog post above. A component of feature engineering is this. This is a very important phase because it improves the machine learning model’s overall quality. Several methods exist for creating features, including scaling, log transform, and OneHot encoding.