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Z-score normalization: Difference between revisions
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==Example== | ==Example== | ||
A feature with the mean of 500 and a standard deviation of 100. | A [[feature]] with the mean of 500 and a standard deviation of 100. | ||
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Let us assume we have a dataset with two features, height (in cm) and weight (in kg), that we would like to apply Z-score normalization to. The data values for these features can be seen in the following table: | Let us assume we have a dataset with two features, height (in cm) and weight (in kg), that we would like to apply Z-score normalization to. The data values for these features can be seen in the following table: | ||
Height (cm) | {| class="wikitable" | ||
| 180 | 85 | 150 | 55 | | | ||
|- | |||
! Height (cm) | |||
! Weight (kg | |||
|- | |||
| 180 || 85 | |||
|- | |||
| 150 || 55 | |||
|- | |||
|} | |||
Before applying Z-score normalization to the dataset, we must first calculate the mean and standard deviation for each feature. These values can be found in the following table: | Before applying Z-score normalization to the dataset, we must first calculate the mean and standard deviation for each feature. These values can be found in the following table: | ||
| Features | {| class="wikitable" | ||
Height (cm) | | | ||
Weight (kg) | |- | ||
! Features | |||
! Mean | |||
! Standard Deviation | |||
|- | |||
| Height (cm) || 166 || 10.954 | |||
|- | |||
| Weight (kg) || 65.6 || 14.834 | |||
|- | |||
|} | |||
By applying the formula for Z-score normalization to each data value in our dataset, we can calculate Z-scores individually. The results are displayed in the following table: | By applying the formula for Z-score normalization to each data value in our dataset, we can calculate Z-scores individually. The results are displayed in the following table: | ||