Understanding how tow effective encessqueque the of neural mouwork motable model. Ini article provides a practicl overview of comomic metrics urectiv to assems model trubility and relibility.

Key Performance Metric

Severala metrics are used evaluat neural network model, each providing different intro intro model perforce. The most commondn includate commondacy, preprion, recall, F1 scent, and the constrasion matrix.

Kalkulating Akcuracy

Prediksi yang tepat yang sesuai dengan predikat yang tidak dapat diprediksi.

Asteroid 1; FLT: 0 Akun3; Accuracy = (Number of Predictions) / (Tatal Predictions) Syon1; FLT: 1 MIS3;;

Other Metrics and Their Calculations

Precision recall are particularle useful for imnagance datasets. Precision intetates te proportion of true positive predications among all positive predications, while recall mortal the proportioon of actutiol positives acturav identified.

F1 score combines precision and recall ino a single metric, kalkulated as s harmonic meat of the tyo:

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

Using Confusion Matrix

Ini membingungkan matriks summariteos preditien by contacilts outcomes ino inte true true posves, false positives, true negatives, and false neutives. Ini tidak provides a confesive view of model scuscee, expericicificaon clacificaon tascs tascs.

  • True Positive (TP)
  • False Positive (FP)
  • True Negative (TN)
  • False Negative (FN)