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Features Scaling in Machine Learning

The document discusses various feature scaling methods in machine learning, including Absolute Maximum Scaling, Min-Max Scaling, Normalization, Standardization, and Robust Scaling. Each method involves specific steps to adjust data values, aiming to improve model performance by addressing issues like outliers and distribution. The document emphasizes the importance of selecting the appropriate scaling technique based on the characteristics of the data.

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32 views5 pages

Features Scaling in Machine Learning

The document discusses various feature scaling methods in machine learning, including Absolute Maximum Scaling, Min-Max Scaling, Normalization, Standardization, and Robust Scaling. Each method involves specific steps to adjust data values, aiming to improve model performance by addressing issues like outliers and distribution. The document emphasizes the importance of selecting the appropriate scaling technique based on the characteristics of the data.

Uploaded by

atharvabhagat935
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Features scaling in machine learning

1. Absolute Maximum Scaling

This method of scaling requires two-step:

1. We should first select the maximum absolute value out of all the entries of a particular
measure.
2. Then after this we divide each entry of the column by this maximum value.

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After performing the above-mentioned two steps we will observe that each entry of the column
lies in the range of -1 to 1. But this method is not used that often the reason behind this is that it
is too sensitive to the outliers. And while dealing with the real-world data presence of outliers is
a very common thing.

 Now let’s apply the first method which is of the absolute maximum scaling. For this first,
we are supposed to evaluate the absolute maximum values of the columns.
 Now we are supposed to subtract these values from the data and then divide the results
from the maximum values as well.

2. Min-Max Scaling
Features scaling in machine learning

This method of scaling requires below two-step:

1. First we are supposed to find the minimum and the maximum value of the column.
2. Then we will subtract the minimum value from the entry and divide the result by the
difference between the maximum and the minimum value.

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3. Normalization

This method is more or less the same as the previous method but here instead of the minimum
value we subtract each entry by the mean value of the whole data and then divide the results by
the difference between the minimum and the maximum value.
Features scaling in machine learning

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4. Standardization

This method of scaling is basically based on the central tendencies and variance of the data.

1. First we should calculate the mean and standard deviation of the data we would like to
normalize it.
Features scaling in machine learning

2. Then we are supposed to subtract the mean value from each entry and then divide the
result by the standard deviation.
This helps us achieve a normal distribution of the data with a mean equal to zero and a
standard deviation equal to 1.

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5. Robust Scaling

In this method of scaling, we use two main statistical measures of the data.
Features scaling in machine learning

 Median
 Inter-Quartile Range

After calculating these two values we are supposed to subtract the median from each entry and
then divide the result by the interquartile range.

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