skaleerimisviisid

Skaleerimisviisid refer to methods used to adjust the size or magnitude of data or processes. In data analysis, skaleerimisviisid are crucial for preparing datasets for machine learning algorithms, ensuring that features with different scales do not disproportionately influence the model. Common skaleerimisviisid include standardization (or Z-score normalization) and min-max scaling.

Standardization transforms data to have a mean of zero and a standard deviation of one. This is achieved by subtracting the mean from each data point and then dividing by the standard deviation. It is particularly useful when the data follows a Gaussian distribution or when the algorithm assumes zero-centered data.

Min-max scaling, on the other hand, rescales data to a fixed range, typically between 0 and 1. The formula involves subtracting the minimum value of the feature from each data point and then dividing by the range (maximum minus minimum value). This method is beneficial when the data needs to be kept within specific bounds, such as for image pixel values or certain neural network activation functions.

Another approach is robust scaling, which uses interquartile range (IQR) to scale data. This method is less affected by outliers compared to standardization or min-max scaling. The choice of skaleerimisviis depends on the specific algorithm being used and the characteristics of the data. Applying appropriate skaleerimisviisid can significantly improve the performance and convergence of many machine learning models.