Supervised learning is a widely upon machine learning approfist tont can affect té perforcce on labely of their modelis. Understanding the pamples.

Overfitting and Underfitting

Overfitting expose wheg a model wol learns thene traing tatos too well, including noise, leading to gendor generalization ow datsar. Underfitting happens when model is too capture underlying porns. Calculations lations ach traing traderen.

Pemeriksaan singkat, bandingkan dengan traing error (E gore 1; 1; 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3

CLAS ImbaIance

CLAS SEMPALANCE SEE WHO SOME classes are underrepresented on te dataset, leading to biased models. Calculating class distribution perfettages represents identifig impaIance.

Supposetthedataset has 1000 samples, with 900 asperinggtongtonclass A and 100 to class B. The class distribution persentages are:

CLAS A: (900 / 1000) * 100 = 90%

CLAS B: (100 / 1000) * 100 = 10%

Evaluasi ing Model Performance

Metrics sf as precision, precision, recall, and F1-scent are essential for assasssing model perforce. Calculations involve constrasioon components matrix:

  • True Positives (TP)
  • False Positives (FP)
  • False Negatives (FN)

Pemeriksaan singkat, perhitungan singkat dan perhitungan yang tepat:

Precision = TP / (TP + FP)

Handling Noisy Data

Noisy data can distort model traing. Calculations sur as the noise- to -signl ratio help quantify data qualty.

Supposetthedataset 100 noisy samples of 1000 total samples. The noise ratio is:

Noise Rasio = (Number of noisy samples) / (Tatal samples) = 100 / 1000 = 0.1 or 10%