Evaluating thee performance of conceped learning models is essential to understand their effectiveness. Various metrics are used to measure how well a model predicts outcomes based on labeled data. These metrics help in comparating models and selecting thee beset one for a specific task.

Common persperance metrics

Several metrics are widely used to assess controled learning models. Thee choice of metric depens on ten he type of problem, such as classification or regression. Understanding these metrics allows for better interpretation of model results.

Metrics for Classification

In classification tasks, common metrics include precisacy, precision, recall, and F1 score. These metrics evaluate different aspects of thee model 's ability to correctly predict class labels.

Přesnost

Akuracy measures thee proportion of correct predictions out of total predictions. It is calculated as:

CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CLAS3c; CCAS3c; CCAS3c; CCAS3c; CCAS3c; CLAS3c; CLAS3c; CLAS3c; CCAS04E004; CLAS3c; C007; CLAS04E007; C007; CCC007; CLAS3c; C007; CCLAS@@

Precision and Recall

Precision indicates the proportion of positive predictions that are correct, while le recall measures the proportion of actual positives correctly identified. They are calculated as:

CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3O3; CLANE3O3 = True Positives / (True Positives + False Positives) CLANE1; CLANE1; CLANE3O3; CLANE3O3;

CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3O3 = True Positives / (True Positives + False Negatives) CLANE1; CLANE1; CLANE3O3;

Metrics for Regression

Regression models are evaluated using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared. These metrics quantify the difference e between predicted and actual continuous values.

Mean Absolute Error (MAE)

MAE calculates thee average absolute difference between predicted and actual values:

CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; MAE = CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CLANE3c; CCANE3c; CLANE3c; CLANE3c; CCANE3c; CLANE3c; CLANE3c; CLANE3c; CLANEDLANEDLANICÍR; CLANEDICÍR; CLANICATIR; CLAND; CLAND; CLAND; CLAND; CLANIV@@

R- squared

R- squared indicates the proportion of variance in thoe dependent variable explicained by te model. It ranges from 0 to 1, with higer values indicating better fit.

These metrics providee a quantitative basis for evaluing and comparating model performance in conceped learning tasks.