Machine Learning - Feature Engineering

This article explains what Feature Engineering is, why it is important, and describes basic Feature Engineering techniques.

What is Feature Engineering? #

Feature Engineering is the process of transforming given raw data into features (or variables) that allow a machine learning model to function effectively. This process includes removing unnecessary information, extracting and transforming useful information, and adjusting the data to improve model performance during training.

The Importance of Feature Engineering #

Feature Engineering can significantly improve the performance of a machine learning model. Good features enable the model to learn patterns in the data better, thus increasing the accuracy of predictions. On the other hand, irrelevant or incorrect features can degrade model performance. Therefore, Feature Engineering is a crucial process for maximizing model performance.

Feature Engineering Techniques #

There are various techniques for Feature Engineering, and below are some of the most basic ones:

1. Missing Value Handling: It is important to deal with missing values in the data. Methods include replacing missing values with the mean, median, mode, or removing rows with missing values.
2. Categorical Data Processing: Many models cannot directly process categorical data. Methods such as One-Hot Encoding and Label Encoding can be used to convert categorical data into numerical data.
3. Feature Scaling: Adjusting the scale of various features allows the model to evaluate features fairly. Standardization and Normalization are examples of this.
4. Feature Selection: Important features are selected to reduce model complexity and prevent overfitting. Statistical methods and model-based methods are examples of this.
5. Feature Creation: New features are created by combining or transforming existing features. This helps the model understand the data better.

Encoding Conversion #

Categorical data refers to data categories represented in text. Since most machine learning algorithms take numerical data as input, converting these categorical data into an appropriate numerical format is essential. Two primary methods are used for encoding conversion.

One-Hot Encoding #

Converts each category into a separate column, assigning a value of 1 if the category is present and 0 otherwise. This method does not consider the order or importance of categories, allowing the model to treat each category equally.

Label Encoding #

Converts each category into a numerical value by assigning sequential numbers. For example, ‘red’, ‘blue’, ‘green’ can be converted to 0, 1, 2, respectively. While Label Encoding does not increase the dimensionality as much as the number of categories, care must be taken as the magnitude of numbers can affect the model.

Scaling #

Feature Scaling is the process of standardizing the units or range of data to a uniform scale, ensuring all features equally influence the model. Two primary methods used for scaling are:

1. Standardization: Adjusts the data to have a mean of 0 and a standard deviation of 1. This method is useful when the data distribution does not follow a normal distribution and is less sensitive to outliers.
2. Normalization: Adjusts the data values to a range between 0 and 1. The most common method uses the minimum and maximum values, ensuring all data points have the same scale.