Ẹkọ Abojuto

O pẹlu classification, ibi ti afojusun ni o wa isori, ati padasehin, ibi ti afojusun ni o wa nọmba. Awọn didara ati itumo ti awọn afojusun akole ni o wa aringbungbun si awọn esi.

Awọn gbigba bọtini

• Ṣalaye awọn aami ṣaaju ki o to gba wọn.
• Jeki awọn igbasilẹ ti o ni ibatan lati jo kọja awọn ipin igbelewọn.
• Ṣe akiyesi awọn awoṣe ti o ṣe pataki si iṣẹ naa.

Kọọkan ikẹkọ apẹẹrẹ so fun awọn alugoridimu ohun ti o wu ti wa ni fẹ fun ohun input. A pipadanu iṣẹ iyipada asọtẹlẹ aṣiṣe sinu kan opoiye awọn ikẹkọ ilana le optimize. The choice of loss shapes learning; the metric used to judge the final workflow may be different. Labels can come from measurements, later results, or annotation. Examine disagreements and ambiguous cases rather than assume every recorded answer is right. If the label captures an old decision process, the model can reproduce that process's limitations. Split the data to match how the model will meet new cases. Random row splits can leak information when repeat records describe the same subject. Forecasts generally need time-respect evaluation. Fit preprocessing steps only on the training partition before applying them to validation and test examples. After training, inspect performance for relevant classes and operating conditions. Class imbalance can make overall accuracy misleading. Decide how uncertain or unfamiliar inputs should be handled, and maintain a route for correcting labels and reviewing systematic mistakes.

1. Ninu idanwo ti a ṣe pẹlu awọn ifiranṣẹ kiakia 40, classifier kan mu 30 ati padanu 10. O tun ṣe afihan awọn ifiranṣẹ arinrin 20.
2. Iranti ifiranṣẹ kiakia jẹ 30/40 = 75%. Konge laarin awọn ifiranṣẹ ti a samisi jẹ 30 / (30 + 20) = 60%.
3. Beere boya atunyẹwo awọn ifiranṣẹ 50 ti a samisi lati wa awọn kiakia 30 wulo fun agbara ati awọn ayo ẹgbẹ naa.

Iṣiro naa ṣe apejuwe iṣẹ iṣaro, kii ṣe itọkasi ọja ti a royin.