Supervised learning systems are evaluaci using variouos metrics tequirlates their etrics esential for assessing model. Understanting how how totilate these metrics esentiaI for assemsing mol perforce.

Kalkulating Akcuracy

Accurachy meths proportion of proportiof predications made by the model of all predications. lt is kalkulated using the formula:

Asteroid 1; FLT: 0 FLT: 0 AC3; Accuracy = (Number of Predictions) / (Tatal Number of Predictions) Aser1; FLT: 1 MIL33; MIL33;;

Pemeriksaan for, if a model dipredikt 90 of 100 instances, the contracy is 0.9 or 90%.

Kalkulating Precision

Precision focuses on the positive ameliby the model. Ini mengindikasikan how many of the predicelete positives are actuala positives.

= (True Positives) / (True Positives + False Positives)

For instance ce, if a model predicts 50 positives, and 40 of these are mengoreksi, the precsion os 0.8 or 80%.

Summary of Calculation Steps

  • Itify té total number of predications and mengoreksi predications for journacy.
  • Menghitung positif dan false positives for precision.
  • Apply the formula to computing each metric.