Z-score normalization: Difference between revisions

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. One widely-used technique for normalization is Z-score normalization (also referred to as standardization).

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.