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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 and formulas to scale data, aiming to improve the performance of machine learning models. The document highlights the importance of choosing the appropriate scaling method based on the presence of outliers and the distribution of the data.

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8 views6 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 and formulas to scale data, aiming to improve the performance of machine learning models. The document highlights the importance of choosing the appropriate scaling method based on the presence of outliers and the distribution 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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Download as DOCX, PDF, TXT or read online on Scribd
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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.

FORMULA

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.

PRACTICAL
Features scaling in machine learning

 First we are supposed to find the minimum and the maximum value of the column.

 Then we will subtract the minimum value from the entry and divide the result by the difference
between the maximum and the minimum value.

PRACTICAL
Features scaling in machine learning

2. Min-Max Scaling

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.

FORMULA

PRACTICAL
Features scaling in machine learning

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.

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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.

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.

FORMULA

PRACTICAL
Features scaling in machine learning

5. Robust Scaling

In this method of scaling, we use two main statistical measures of the data.

 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.

FORMULA

PRACTICAL
Features scaling in machine learning

PRACTICAL

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