Understanding Z Score in Machine Learning
If you’re involved in the field of machine learning, then you must have come across the term ‘Z score’ quite often. Z score is a statistical measurement that helps us understand how far any given data point is from the mean of the data set. It finds its usage in a myriad of applications in machine learning, right from data preprocessing to anomaly detection.
In this article, we’ll delve deep into the concept of Z score and learn how it works in the realm of machine learning.
What is a Z score?
The Z score is a standard score that indicates how many standard deviations an observation or data point is from the mean value in a data set. It’s also called the standard score or the normalized value.
A Z score can be calculated for any data point in a given data set, and it provides a standardized way of comparing a given data point with the rest of the data in the set. A positive Z score indicates that the data point is above the mean, while a negative Z score indicates that it’s below the mean.
How is Z score calculated?
To calculate the Z score of a data point, we use the following formula:
Z = (x-μ)/σ
Where:
x = The data point we want to find the Z score for
μ = The mean of the entire data set
σ = The standard deviation of the entire data set
For instance, suppose we have the following data set:
[10, 15, 20, 25, 30]
The mean value of the data set is:
(10+15+20+25+30)/5 = 20
The standard deviation of the data set is calculated as:
sqrt(((10-20)^2 + (15-20)^2 + (20-20)^2 + (25-20)^2 + (30-20)^2)/5) = 7.9057
Now, let’s calculate the Z score for the data point ’25’ using the formula:
Z = (25-20)/7.9057 = 0.632
Why do we use Z score in machine learning?
Z score has various uses in machine learning because of its ability to provide us with standardized measurements in a data set. It helps us to standardize the data across different scales and make it easier to compare different variables.
Z score is particularly useful in data preprocessing as we can use it to identify outliers or anomalies in our data. These anomalies can be a result of errors in data collection or measurement and can significantly impact our model’s accuracy. By filtering out the outliers, we can improve the overall performance of our model.
Conclusion
In summary, Z score is a statistical measurement that allows us to understand how far any given data point is from the mean of a data set. Its usage in machine learning is widespread, from data preprocessing to anomaly detection and outlier analysis.
By understanding how Z score works, we can improve the accuracy of our machine learning models by identifying and removing outliers that may adversely impact our predictions.
