Quantilistransformaatio

Quantilistransformaatio, also known as quantile transformation, is a data preprocessing technique used in machine learning and statistics. It involves mapping data to a uniform distribution or a normal distribution by utilizing the quantiles of the original data. This transformation can be particularly useful for algorithms that are sensitive to the distribution of the input features, such as linear models or support vector machines.

The process typically involves calculating the cumulative distribution function (CDF) of the data and then applying the inverse CDF of the target distribution (e.g., uniform or normal) to the original data points. Alternatively, one can first rank the data points and then map these ranks to the desired distribution. This approach ensures that the relative order of the data points is preserved, while their distribution is altered.

Quantile transformation can help to reduce the impact of outliers and make the data more amenable to certain types of analysis. It is often applied when dealing with skewed data or when features have vastly different scales. However, it's important to note that this transformation is non-linear and can distort the relationships between variables, so it should be used judiciously. The choice of target distribution, uniform or normal, depends on the specific requirements of the subsequent modeling task.