modeltilpasning
Modeltilpasning, often translated as model fitting or model adaptation, is a core concept in statistical modeling and machine learning. It refers to the process of adjusting the parameters of a model so that it best represents the observed data. This involves finding the values for the model's coefficients or weights that minimize the difference between the model's predictions and the actual data points.
The goal of modeltilpasning is to create a model that not only accurately describes the training data
There are two main concerns related to modeltilpasning: underfitting and overfitting. Underfitting occurs when a model
Techniques like cross-validation are employed to assess how well a model generalizes and to help strike a