Machine learning is a modern yet essential piece of the digital transformation and data analytics processes. On the other hand, feature transformation is the process of modifying data but keeping the information that data provides. Data modifications like these will make understanding machine learning (ML) algorithms easier, delivering better results.
This article will discuss the importance of feature transformation, a crucial step in the preprocessing stage. Feature transformation allows for the maximum benefit of the dataset’s features and the long-term success of the application or model.
Applying various mathematical techniques to existing application features can result in new features or feature reduction. Modifying existing data and increasing the available information and background experience can increase the model’s success by keeping the information constant.
The Need for Feature Transformation
You might find yourself asking why feature transformation is necessary in the first place. The need becomes more apparent when you understand that if you have too few features, your model will not have much to learn from, while too many features can feed a plethora of unnecessary information. The goal is to be somewhere in the middle.
Data scientists often work with datasets that contain various columns and different units within each column. For example, one column might be centimeters while the other is kilograms. So you can see the range, we’ll use another example of income, with columns ranging from $20,000 to $100,000 or more. Age is another factor with many variables, ranging from 0 to upward of 100.
So, how can we be sure that we’re treating these variables equally when dealing with machine learning models? When feeding features to a model, there is a chance that the income will affect the result because it has a more significant value. However, it doesn’t mean that it’s a more important predictor. To give importance to all variables, feature transformation is necessary.
How to Identify Variable Types
We’ve touched on how feature transformation can affect variables’ effect on an outcome, but how can we determine variable types? We can typically characterize numerical variables into four different types.
When you begin a project based on machine learning, it’s essential to determine the type of data in each feature because it could severely impact how your machine learning models perform. Here are four variable types in feature transformation for machine learning.
Data Preparation
Feature transformation is a mathematical transformation, and the goal is to apply a mathematical equation and then transform the values for our further analysis. Before we do this, however, it’s crucial to prepare the data you’ll be changing.
Analyzing data without preparation is impossible, and you can’t apply genuine feature transformation without examining. So, here are the steps you should take to prepare your data for feature transformations.
The Goal of Feature Transformation
The goal of feature transformation is to create a dataset where each format helps improve your AIML models’ performance. Developing new features and transforming existing features will significantly impact the success of your ML models, and it’s important to think logically about how to treat your prepared, collected data and the current list of variables you have.
When you enter the model-building phase, you should go back and alter your data by utilizing various methods to boost model accuracy. Collecting and taking the time to ensure your data is ready for transformation will reduce the time you spend returning to the transformation stages.
Feature transformation will always be beneficial for further analysis of collected data and changing how our machine learning models operate. Still, knowing how to prep your data and categorize it is crucial, so your transformations provide accurate, helpful, eye-opening results.
