Machine Learning for OpenCV

Standardizing features

Standardization refers to the process of scaling the data to have zero mean and unit variance. This is a common requirement for a wide range of machine learning algorithms, which might behave badly if individual features do not fulfill this requirement. We could manually standardize our data by subtracting from every data point the mean value (μ) of all the data, and dividing that by the variance (σ) of the data; that is, for every feature x, we would compute (x - μ) / σ.

Alternatively, scikit-learn offers a straightforward implementation of this process in its preprocessing module. Let's consider a 3 x 3 data matrix X, standing for three data points (rows) with three arbitrarily chosen feature values each (columns): ...