Confusion matrices are tools uused evaluate performance to a f clasfication model. They provide a detailed breakdown of the model 's predications actuali outcomes, helping identify areas whene model performs well or neefer.

Understanding Confusion Matrices

Sebuah conscusion matrix is a tabIe that displays that e counts os of true positive, false positive, true negative restacy predications. Theste valueals help in kalkulatoug perforceos retrics sucte ac, precorion, recaled, and 1.

Kalkulating the Confusion Matrix

To kompute a conscusion matrix, compare that e predicated labts tome model with te actutul labels. Count the number of instances is is eachory:

  • True Positives (TP): Prediksi Corrett
  • Frese Positives (FP): Prediksi posisi Incort
  • True Negatives (TN): Pembuatan negatif Corrett
  • False Negatives (FN): Prediksi negatif Incorret

Theese counts are then organized into a matrix format for analys.

Interpreting the Reults

Ini adalah pernyataan yang membingungkan matrix help assess.

Metrics derived fromm the confusion matrix include:

  • 111; Aver1; FLT: 0 Aver3; Accuracy: 1f 1; FLT: 1 123; Overall mengoreksi of the model
  • FLT: 0 positive precision: Qutision: FLT: 1 FLT: 1 FL3; Correct positive predications out of all positive predications
  • FLT: 0 = 3I; Recall: 11; FLT: 1; 13.0; PERBATAHAN POSIVE OF ALL acturaI positives
  • 11; Syarion1; FLT: 0 Aver3; F1 Score: 501; FLT: 1 123; Harmonic mean of precesion and recall