sannolikhetsmatris

Sannolikhetsmatris is a term often encountered in statistical modeling and machine learning, particularly when dealing with classification problems. It is a square matrix that represents the probabilities of different class outcomes for a given set of predictions. More specifically, it is a confusion matrix where each cell contains the probability of observing a particular true class given a predicted class, or vice-versa, depending on the convention used. The rows typically represent the true classes, and the columns represent the predicted classes, or the other way around. The sum of probabilities across each row or column, depending on the definition, will usually equal one, indicating a complete probability distribution.

In practice, a sannolikhetsmatris is derived from a set of test data where both the true labels and the predicted labels are known. By counting the occurrences of each combination of true and predicted labels and then normalizing these counts, the probabilities can be calculated. For example, if the matrix entry at row i and column j is P(True Class i | Predicted Class j) = 0.7, it means that when the model predicts Class j, there is a 70% probability that the true class is actually Class i. This type of matrix is valuable for understanding the performance of a classification model, identifying specific types of errors, and comparing different models. It provides a more nuanced view than simple accuracy by detailing the interrelationships between predicted and actual classes.