What is Feature Engineering? Definition | AI Understanding

Itumọ

Ṣiṣeto tabi yiyipada awọn oniyipada titẹ sii lati jẹ ki ẹkọ rọrun ati imunadoko diẹ sii.

Awọn ofin ti o jọmọ

Ẹya ara ẹrọ

Oniyipada igbewọle ti a lo nipasẹ awoṣe lati ṣe awọn asọtẹlẹ.

Iṣẹ-ṣiṣe nibiti awoṣe kan ti n fi igbewọle si ọkan tabi diẹ ẹ sii awọn ẹka ti a ti ni asọye.

Prompt Engineering

Ṣiṣeto awọn itọka lati mu didara iṣelọpọ pọ si, igbẹkẹle, ati iṣakoso.

Iye owo afikun ni akoko, iṣiro, tabi iyara ọja ti o nilo lati jẹ ki awọn ọna ṣiṣe ni ailewu ati iṣakoso diẹ sii.

Yiyipada data aise sinu awọn ẹya alaye ti awoṣe le lo.

Ile itaja ẹya

Eto iṣakoso fun titoju ati ṣiṣe awọn ẹya ML ti a fọwọsi ni igbagbogbo fun ikẹkọ ati itọkasi.

Kọ ẹkọ diẹ sii ninu awọn itọsọna ọfẹ wa

Prompt Engineering Linear Probing and Frozen Feature Evaluation Feature Engineering Pipelines and Data Versioning Online and Offline Feature Serving Skew

Wo tun

False Positive Few-Shot Learning False Negative Fine-Tuning Explainability Foundation Model Evaluation Set Function Calling

Ṣawakiri ni kikun AI Gilosari Ṣawari gbogbo awọn itọsọna AI ọfẹ