ML_Concepts

Posted on

# Decimal Scaling - Another Data Normalization Technique?

Note: use this link to check out our original article on Decimal Scaling in data mining.

Decimal scaling is a pre-processing technique used in machine learning to scale the values of input features. This technique aims to bring the values of the features within a certain range, typically between -1 and 1, to make them more manageable for the machine learning model. This can be especially useful when the values of the input features have a wide range, as this can cause problems for certain types of models, such as neural networks.

The technique involves multiplying each value in the feature set by a power of 10, usually 10^n, where n is a positive or negative integer. This is done to change the scale of the values, to make them more consistent and easier to work with. For example, if a feature has values that range from 0 to 1,000,000, multiplying by 10^-6 will bring the values down to a range of 0 to 1. Similarly, if a feature has values that range from 0 to 0.0001, multiplying by 10^4 will bring the values up to a range of 0 to 1.

There are several benefits to using decimal scaling in machine learning. One of the most important is that it can help to improve the performance of the model. This is because many machine learning algorithms, such as neural networks, are sensitive to the scale of the input features. If the values of an attribute are too large or too small, this can cause problems with the training and evaluation of the model. By scaling the values of the features, decimal scaling can help to ensure that the model is working with a consistent and manageable set of input data.

Another benefit of decimal scaling is that it can help to reduce the risk of overfitting. Overfitting is a common problem in machine learning, and it occurs when the model is too closely fit to the training data, resulting in poor generalization performance. By scaling the values of the features, decimal scaling can help to reduce the risk of overfitting by making the model less sensitive to slight variations in the input data.

However, it is important to note that decimal scaling should be used with caution. It does not apply to all datasets and models. For example, if the data is already in a consistent range, there is no need to perform the scaling. Additionally, the scaling factor should be chosen carefully, as an inappropriate scaling factor can lead to poor performance of the model. It is also important to note that decimal scaling should only be applied to the input features, and not the output variable.

In conclusion, decimal scaling is a preprocessing technique used in machine learning to scale the values of input features. The goal of this technique is to bring the values of the features within a certain range, typically between -1 and 1, to make them more manageable for the machine learning model. This technique can be especially useful when the values of the input features have a wide range, as this can cause problems for certain types of models. However, it is important to use decimal scaling with caution and it should be applied only if it is necessary.
If you liked this article then check out our other articles on Normalization in machine-learning models.

Min-Max-Normalization

Z-Score Normalization

Summary
In this article, I tried to explain decimal scaling in simple terms. If you have any questions about the post, please put them in the comment section and I will do my best to answer them.