Z-score normalization: Difference between revisions

Z-score normalization is a type of data scaling that transforms data values to have a mean of zero and standard deviation of one. This transformation occurs by subtracting the mean from each value and dividing by its standard deviation. The results are known as Z-scores, which indicate how far away from the mean each data point is.

Data normalization in machine learning is a critical preprocessing step that helps boost the performance of many algorithms. Normalization involves scaling data to a specified range or distribution to reduce the impact of differences in scale or units of features.

Z-score normalization is a technique commonly used in machine learning to address the issue of feature scaling. When features in a dataset have different scales or units, it can cause issues for certain machine learning algorithms that rely on distance-based calculations such as k-nearest neighbors (KNN) or support vector machines (SVM), which require equal weighting across all features in the analysis. With Z-score normalization, however, we can standardize these dimensions so that each contributes equally to our analysis.

$z = (x - mu) / sigma$

Where: ($z$ is the Z-score for a particular data value); ($x$ is its original data value); and ($mu$ stands for mean of all features in that feature; while $sigma$ stands for standard deviation of those data values).

1. Calculate the mean and standard deviation for each feature in the dataset.

2. For each data value within a feature, subtract its mean value and divide by its standard deviation.

3. These values correspond to Z-scores for each data point.