normaalikäsittelyllä

Normaalikäsittelyllä refers to a method of data normalization or standardization. In statistical and machine learning contexts, normalization is a technique used to scale numerical data to a standard range or distribution. This is often done to improve the performance of algorithms that are sensitive to the scale of input features, such as gradient descent-based methods or distance-based algorithms. Common normalization techniques include min-max scaling, where data is scaled to a range like 0 to 1, and z-score standardization, where data is transformed to have a mean of 0 and a standard deviation of 1. The choice of normalization method can depend on the specific dataset and the requirements of the subsequent analysis or model. Applying normaalikäsittelyllä can help prevent features with larger values from dominating those with smaller values, leading to more robust and accurate results. It is a fundamental preprocessing step in many data science workflows.