normaaliinastamisen
Normaaliinastaminen, also known as normalization or standardization, is a data preprocessing technique used to scale numerical attributes in a dataset. The goal is to transform the data so that it has a mean of zero and a standard deviation of one. This is achieved by subtracting the mean of the feature from each data point and then dividing by the standard deviation of that feature.
This process is crucial in many machine learning algorithms, especially those that are sensitive to the scale
The formula for z-score normalization is as follows: z = (x - μ) / σ, where x is the original data
Another common normalization technique is min-max scaling, which scales data to a fixed range, typically between
Choosing between different normalization methods depends on the specific algorithm and the characteristics of the data.