Understanding Machine Learning Normalization Techniques
Machine learning has been increasingly making its way into all kinds of industries in recent times. But to fully harness the power of machine learning algorithms, we need to prepare our data for it. In this article, we will discuss normalization, an essential step in preparing data for machine learning. Let’s dive in!
What is Normalization?
Normalization is the process of transforming numerical data so that it fits within a specified range. This process ensures that larger values in one feature do not dominate smaller values in another.
In the field of machine learning, normalization is critical to ensure that our algorithms work effectively. Without normalization, some input features could be more valuable than others, leading to biased models.
Types of Normalization Techniques
There are several normalization techniques, let’s take a look at some of them.
Min-Max Normalization
In min-max normalization, we transform the numerical values in our dataset to fit between a specific minimum and maximum value. To do this, we subtract the minimum value from the number and divide it by the range (the difference between the maximum and minimum values).
For example, suppose we have a dataset containing the ages of people between 0 and 100. We could normalize the data between 0 and 1 by using the following formula:
normalized_age = (age – 0) / (100 – 0)
Z-Score Normalization
Z-score normalization (also known as standardization) scales numerical data so that the mean is set to 0, and the standard deviation is set to 1. This technique assumes that the data is normally distributed and works best when the data ranges are not known in advance.
The formula for z-score normalization is:
normalized_value = (value – mean) / standard_deviation
When to Use Normalization Techniques
Normalization techniques are useful when we are working with different-sized data and want to standardize them, so they are comparable. It is particularly crucial when we are comparing data with different units or range.
However, it is not always necessary to normalize data. If all features in our dataset have similar scales and distribution, then normalization may not be necessary.
Conclusion
In this article, we discussed normalization, an essential process in preparing data for machine learning. Different normalization techniques, such as min-max and Z-score normalization, help to standardize data and make it comparable. Remember, normalization is not always necessary, so it is crucial to understand when and when not to apply it. Happy data wrangling!
