December 2024
Beginner to intermediate
336 pages
7h 41m
Korean
Content preview from 로우코드 AI
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181
6
장
빅쿼리 ML로 선형 회귀 모델 학습시키기
6.3.3
레이블과 특징의 관계 그리기
EDA
로 특징과 레이블 사이의 관계를 시각화하면 모델에 가장 유용한 특징을 파악할 수 있습
니다. 앞서 사용한 코랩 노트북에 이어서 시각화를 진행합니다.
먼저 신규 셀에 다음 코드를 추가하고 실행해
Temp
특징과
Energy
_
Production
레이블 사이
의 관계를 시각화합니다. [그림
6
-
19
]의 시각화와 비교하여 결과를 확인합니다.
ax = sns.regplot(
x='Temp', y='Energy_Production',
fit_reg=False, ci=None, truncate=True, data=results)
ax.figure.set_size_inches(10,8)
그림
6-19
Temp
와
Energy
_
Production
의 관계를 시각화한 산점도. 음의 선형 관계가 드러납니다.
이제
Ambient
_
Pressure
특징과
Energy
_
Production
레이블 사이의 관계도 시각화해 봅니다.
먼저 코드를 직접 작성해 보기 바랍니다. 만약 코드 작성에 어려움을 느낀다면 다음 코드를 참
조하세요. [그림
6
-
20
]의 시각화와 비교하여 결과를 확인합니다.
182
로우코드 AI
ax = sns.regplot(
x='Ambient_Pressure', y='Energy_Production', ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.