쉐도잉 연습: Ridge vs Lasso Regression, Visualized!!! - 영상으로 영어 말하기 배우기

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Ridge, regression versus lasso regression, which one will survive?

StatQuest.

Hello, I'm Josh Starmer and welcome to StatQuest.

Today we're going to talk about ridge versus lasso regression and the differences are going to be visualized.

Note, this StatQuest assumes that you are already familiar with ridge and lasso regression.

If not, check out the quests.

To show you the difference between ridge and lasso regression, we're going to use a very simple data set that consists of weight and height measurements.

And we'll start by fitting this horizontal line to the data.

This horizontal line represents a terrible fit.

And we can measure how bad that fit is by calculating residuals, the difference between the observed and predicted values.

In order to compare the horizontal line to other lines fit to the data, we will plot the sum of the squared residuals on a graph.

The y-axis on this graph is the sum of the squared residuals,

and the x-axis represents different slope values for the fitted line.

In this case, the slope for the horizontal line is 0.

Now let's increase the slope to 0.2, and calculate a new value for the sum of the squared residuals.

Now let's increase the slope to 0.4, and calculate a new value for the sum of the squared residuals.

We can keep plugging in new values for the slope and plotting the sum of the squared residuals,

Or we can just plot the curve for this equation.

We can see that the best fitting line is at the bottom of the parabola.

In other words, when the slope equals 0.45, we get the lowest sum of the squared residuals.

In this example, we simply calculated the sum of the squared residuals for different slopes.

Now let's add the ridge regression penalty, aka the L2 norm.

if you asked me, it should be called the squared penalty, since that's what it is, and, for me, way easier to remember.

The thick blue line that we just drew represents lambda equals zero.

This is because when lambda equals zero, the penalty is equal to zero,

regardless of the slope, and we are left with the original sum of squared residuals.

Now let's see what happens when we set lambda equal to 10.

And just like before, we'll start with a horizontal line, only this time, the line is orange.

Just like before, we can calculate the residuals.

And we can calculate the sum of the squared residuals plus lambda times the slope squared.

In this case, lambda equals 10.

And the slope of the horizontal line is 0.

So the penalty is 0.

So we plot the sum of the squared residuals here.

Now let's increase the slope to 0.2.

Note, the residuals are smaller than before, so the sum of squared residuals is smaller than before.

But now the penalty is 0.4.

And that gives us this point on the graph.

Now let's increase the slope to 0.4.

And the residuals are even smaller.

But now the penalty is 1.6.

And that gives us this point on the graph.

And like we did before, we can keep plugging in new values for the slope and plotting the sum of the squared residuals plus the penalty,

or we can just plot the curve with a thick orange line that represents lambda equals 10.

The bottom of the parabola is where the slope gives us the lowest sum of squared residuals plus penalty,

and that corresponds to this specific line.

And when we compare that to the optimal slope when lambda equals zero,

we see that setting lambda equal to 10 results in a smaller optimal slope.

Note, we can also see that when lambda equals 10, the lowest point in the parabola is closer to zero than when lambda equals zero.

So, either way we look at it, we see that the larger value for lambda shrunk the optimal value for the slope.

Likewise, the thick green line represents lambda equals 20.

We see that the minimum value is closer to zero, and the optimal slope has shrunk some more.

The purple lines represent lambda equals 40, and it shrinks the slope even more.

In other words, as we increase lambda for the ridge regression penalty, a.k.a the L2 penalty, a.k.a the square penalty,

The optimal slope gets closer and closer to zero, but it does not equal zero.

Bam!

Now let's see what happens if we use the lasso penalty, a.k.a the L1 norm, or, if you asked me,

I'd call it the absolute value penalty.

Unfortunately, no one asked me.

Again, the thick blue line represents lambda equals zero, so there is no extra penalty.

This is because when we plug lambda equals zero into the equation, the penalty becomes zero.

And we are left with the original sum of the squared residuals.

Note, just like before, we'll keep track of the best fitting line plus penalty in this graph on the left.

The thick orange line represents lambda equals 10, so now we are turning on the penalty and shrinking the slope.

When lambda equals 10, we start to see a kink in the curve where the slope is 0.

The thick green line represents lambda equals 20, and this kink at zero is becoming more prominent.

Lastly, the thick purple line represents lambda equals 40.

And now the kink at zero is super obvious.

Now the lowest point in the purple curve,

aka the optimal slope given the absolute value penalty when lambda equals 40, is zero.

And that means the slope of the optimal line is 0.

And that means when lambda equals 40, we ignore weight as a variable when predicting height.

Double bam!

In summary, when we increase the ridge regression penalty, a.k.a the L2 penalty, a.k.a the square penalty,

the optimal slopes shift towards zero, but we retain a nice parabola shape.

And even when we set lambda to something crazy high like 400, we still end up with an optimal value greater than zero.

In contrast, when we increase the lasso penalty, a.k.a the L1 penalty, a.k.a the absolute value penalty,

The optimal value shifts towards zero, but since we have a kink at zero, zero ends up being the optimal slope.

Bam!

Hooray!

We've made it to the end of another exciting StatQuest.

If you like this StatQuest and want to see more, please subscribe.

And if you want to support StatQuest, consider contributing to my Patreon campaign,

becoming a channel member, buying one or two of my original songs or a t-shirt or a hoodie or just donate.

The links are in the description below.

All right, until next time, quest on!

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이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “Ridge vs Lasso Regression, Visualized!!!”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: slope, penalty, lambda, equal, zero. 이 영상에는 섀도잉할 문장 86개와 단어 1111개가 있습니다. 말하는 구간의 길이는 9:04입니다. 화자는 분당 약 122단어의 일정한 속도로 말해서 섀도잉하기에 편한 속도입니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 70%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
penalty 명사	/ˈpɛn.əl.ti/	처벌, 형벌
thick 형용사	/θɪk/	두껍다
horizontal 형용사	/ˌhoɹəˈzɑn.təl/	가로의, 수평의
calculate 동사	/ˈkælkjʊleɪt/	정산하다, 계산하다
graph 명사	/ɡɹæf/	그래프, 도표
curve 명사	/kɚv/	곡선
purple 명사	/ˈpɝ.pl̩/	자주색, 보라색
plug 명사	/plʌɡ/	플러그
compare 동사	/kəmˈpɛɚ/	비교하다, 비유하다
versus	/ˈvɝsəs/	대
quest 명사	/kwɛst/	탐색, 탐구
predict 동사	/pɹɪˈdɪkt/	예언하다, 예측하다
axis 명사	/ˈæksɪs/	축
equation 명사	/ɪˈkweɪ.ʒən/	방정식
norm 명사	/nɔːm/	규범

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	be visualized · should be called · are left
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	has shrunk · We've made

주의할 발음

화자는 we'll, we're, I'd 같은 축약형과 약화된 형태를 8번 사용합니다. 들리는 대로 짧게 발음하세요.

“sh”와 “zh” 소리: equation /ɪˈkweɪ.ʒən/, contrast /kənˈt(ʃ)ɹæst/, measurement /ˈmɛʒ.ə.mənt/, regression /ɹiːˈɡɹɛʃ.ən/, visualize /ˈvɪʒuəˌlaɪz/
긴 단어 — 강세 위치에 주의: residual /ɹɪˈzɪd͡ʒ.u.əl/, horizontal /ˌhoɹəˈzɑn.təl/, parabola /pəˈɹæb.ə.lə/

한국어 화자가 어려워하는 소리:

/v/ — ㅂ(/b/)과 구별하기: curve /kɚv/, versus /ˈvɝsəs/, variable [ˈvæɹ.i.ə.bl̩], observe /əbˈzɜːv/, visualize /ˈvɪʒuəˌlaɪz/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: residual /ɹɪˈzɪd͡ʒ.u.əl/, horizontal /ˌhoɹəˈzɑn.təl/, observe /əbˈzɜːv/, likewise /ˈlaɪkˌwaɪz/, visualize /ˈvɪʒuəˌlaɪz/
/r/과 /l/ 구별하기: residual /ɹɪˈzɪd͡ʒ.u.əl/, horizontal /ˌhoɹəˈzɑn.təl/, regardless /ɹɪˈɡɑɹd.lɪs/, variable [ˈvæɹ.i.ə.bl̩], parabola /pəˈɹæb.ə.lə/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
보통 속도로 한 문장씩 섀도잉하고, 화자의 리듬과 맞을 때까지 반복합니다.
자신의 목소리를 녹음해 원본과 비교하고, penalty, thick, horizontal 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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쉐도잉 연습: Ridge vs Lasso Regression, Visualized!!! - 영상으로 영어 말하기 배우기

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