#47 Machine Learning & Data Science Challenge 47
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
What is the difference between normalization and Standardization with example?
In Machine Learning, every practitioner knows that feature scaling is an important issue.
The two most discussed scaling methods are Normalization and Standardization.
Normalization typically means it rescales the values into a range of [0,1].
It is an alternative approach to Z-score normalization (or standardization) is the so-called Min-Max scaling (often also called “normalization” - a common cause for ambiguities).
In this approach, the data is scaled to a fixed range - usually 0 to 1. Scikit-Learn provides a transformer called MinMaxScaler for this.
A Min-Max scaling is typically done via the following equation:
$$Xnorm = X-Xmin/Xmax-Xmin$$
Example with sample data:
Before Normalization:
Attribute Price in Dollars Storage Space Camera
Mobile 1 250 16 12
Mobile 2 200 16 8
Mobile 3 300 32 16
Mobile 4 275 32 8
Mobile 5 225 16 16
After Normalization:
Attribute Price in Dollars Storage Space Camera (Values range from 0-1 which is working as expected)
Mobile 1 0.5 0 0.5
Mobile 2 0 0 0
Mobile 3 1 1 1
Mobile 4 0.75 1 0
Mobile 5 0.25 0 1
Standardization (or Z-score normalization) typically means rescales data to have a mean of 0 and a standard deviation of 1 (unit variance) Formula:
- Z or X_new=(x−μ)/σ where μ is the mean (average), and σ is the standard deviation from the mean; standard scores (also called z scores)
Scikit-Learn provides a transformer called StandardScaler for standardization Example:
Let’s take an approximately normally distributed set of numbers: 1, 2, 2, 3, 3, 3, 4, 4, and 5. Its mean is 3, and its standard deviation is 1.22.
Now, let’s subtract the mean from all data points.
we get a new data set of -2, -1, -1, 0, 0, 0, 1, 1, and 2.
Now, let’s divide each data point by 1.22. As you can see in the picture below, we get -1.6, -0.82, -0.82, 0, 0, 0, 0.82, 0.82, and 1.63.
