May 2018
Beginner to intermediate
672 pages
16h 54m
Korean
Content preview from 핸즈온 머신러닝: 사이킷런과 텐서플로를 활용한 머신러닝, 딥러닝 실무
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193
4
장
모델 훈련
정 경계
decision
boundary
가 만들어집니다. 꽃잎 너비가
1
.
6cm
보다 크면 분류기는
Iris
-
Verginica
로 분류하고, 그보다 작으면 아니라고 예측할 것입니다(아주 확실하지 않더라도 ).
>>>
log
_
reg
.
predict
([[
1
.
7
],
[
1
.
5
]])
array
([
1
,
0
])
[그림
4
-
24
]는 같은 데이터셋을 이번에는 꽃잎 너비와 꽃잎 길이 두 개의 특성으로 보여줍
니다. 훈련이 끝나면 로지스틱 회귀 분류기가 이 두 특성을 기반으로 하여 새로운 꽃이
Iris
-
Verginica
인지 확률을 추정할 수 있습니다. 점선은 모델이
50
% 확률을 추정하는 지점으로,
이 모델의 결정 경계입니다. 이 경계는 선형임을 주목하세요.
45
15
%(왼쪽 아래)부터
90
%(오
른쪽 위)까지 수평한 직선들은 모델이 특정 확률을 출력하는 포인트를 나타냅니다. 모델은 맨
오른쪽 위의 직선을 넘어서 있는 꽃들을
90
% 이상의 확률로
Iris
-
Virginica
라고 판단할 것입
니다.
그림
4-24
선형 결정 경계
꽃잎 길이
꽃잎 너비
Iris
‐
Verginica
Iris
‐
Verginica 아님
다른 선형 모델처럼 로지스틱 회귀 모델도
1
,
2
페널티를 사용하여 규제할 수 있습니다. 사이
킷런은
2
페널티를 기본으로 합니다.
NOTE
_
사이킷런의
LogisticRegression ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.