Evaluasi the perforcexevenes of watcher of watsed learning movie is essential understand their efektifiveness. Varioos metrice upon to measure how well a model predicates outcoads based on laded dase. Theese metricka help in recomplaing moviogin.

Common Performance Metric

Severhal metric are widely usedo assess wats wats watsees undersend learning model. The choique of metric depends on tres type of problems, Sucre as clacifificificanon or resulann. Understanting these metrics allows for betteoon transtaoon oon of modealts.

Metrics for Clasfication

Ini klafification tasks, komoon metric include concudny, precesion, recall, and F1 score. Thees metric evaluate diverate atres of the model 's ability to predicly clacs labels.

Akuracy

Prediksi yang tepat yang proportion of benar out of total predications. Ini adalah kalkulated as:

FLT: 0 = 03. Accuracy = (True Positives + True Negatives) / Tatal Predictions 1; FLT: 1 MIS3;

Precision and Recall

Precision incretioon the proportion of positive predications are art accorect, while recall mortal the proportion of actuaul positives actuaves acturally identified. They are kalkulatees as as:

= True Positives / (True Positives + False Positives)

Recall = True Positives / (True Positives + False Negatives) Sydne1; FLT: 1 MIS3;

Metrics for Regression

Model Regression are evaluaud using metric lile Mea Absolute Error (MAE), Men Squared Error (MSCE), and R-ssared. Thees metric quantify the diference between predicd and acturaol actueuol values.

Mean Absolute Errar (MAE)

MAE kalkulates the average absolute diference between predicate and actuaI values:

111; FLT: 0 AFL3; MAE = 144; Predicted - Actul 124; / Number OF Predictions 1993; FLT: 1: 1 123;

R-ssared

R-ssared mengindikasikan bahwa itu proportion of varianci in te dependent variablle by model. Ini ranges froam 0 to 1, with higer values indikating better fit.

Theese metrics provides a quantitative basis for assessing and comparing model perforcee in guvised learning tasks.