Shadowing Practice: Ridge vs Lasso Regression, Visualized!!! - Learn English Speaking with Video
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Ridge, regression versus lasso regression, which one will survive?
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StatQuest.
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Hello, I'm Josh Starmer and welcome to StatQuest.
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Today we're going to talk about ridge versus lasso regression and the differences are going to be visualized.
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Note, this StatQuest assumes that you are already familiar with ridge and lasso regression.
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If not, check out the quests.
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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.
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And we'll start by fitting this horizontal line to the data.
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This horizontal line represents a terrible fit.
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And we can measure how bad that fit is by calculating residuals, the difference between the observed and predicted values.
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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.
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The y-axis on this graph is the sum of the squared residuals,
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and the x-axis represents different slope values for the fitted line.
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In this case, the slope for the horizontal line is 0.
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Now let's increase the slope to 0.2, and calculate a new value for the sum of the squared residuals.
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Now let's increase the slope to 0.4, and calculate a new value for the sum of the squared residuals.
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We can keep plugging in new values for the slope and plotting the sum of the squared residuals,
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Or we can just plot the curve for this equation.
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We can see that the best fitting line is at the bottom of the parabola.
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In other words, when the slope equals 0.45, we get the lowest sum of the squared residuals.
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In this example, we simply calculated the sum of the squared residuals for different slopes.
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Now let's add the ridge regression penalty, aka the L2 norm.
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if you asked me, it should be called the squared penalty, since that's what it is, and, for me, way easier to remember.
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The thick blue line that we just drew represents lambda equals zero.
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This is because when lambda equals zero, the penalty is equal to zero,
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regardless of the slope, and we are left with the original sum of squared residuals.
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Now let's see what happens when we set lambda equal to 10.
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And just like before, we'll start with a horizontal line, only this time, the line is orange.
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Just like before, we can calculate the residuals.
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And we can calculate the sum of the squared residuals plus lambda times the slope squared.
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In this case, lambda equals 10.
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And the slope of the horizontal line is 0.
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So the penalty is 0.
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So we plot the sum of the squared residuals here.
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Now let's increase the slope to 0.2.
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Note, the residuals are smaller than before, so the sum of squared residuals is smaller than before.
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But now the penalty is 0.4.
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And that gives us this point on the graph.
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Now let's increase the slope to 0.4.
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And the residuals are even smaller.
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But now the penalty is 1.6.
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And that gives us this point on the graph.
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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,
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or we can just plot the curve with a thick orange line that represents lambda equals 10.
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The bottom of the parabola is where the slope gives us the lowest sum of squared residuals plus penalty,
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and that corresponds to this specific line.
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And when we compare that to the optimal slope when lambda equals zero,
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we see that setting lambda equal to 10 results in a smaller optimal slope.
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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.
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So, either way we look at it, we see that the larger value for lambda shrunk the optimal value for the slope.
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Likewise, the thick green line represents lambda equals 20.
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We see that the minimum value is closer to zero, and the optimal slope has shrunk some more.
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The purple lines represent lambda equals 40, and it shrinks the slope even more.
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In other words, as we increase lambda for the ridge regression penalty, a.k.a the L2 penalty, a.k.a the square penalty,
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The optimal slope gets closer and closer to zero, but it does not equal zero.
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Bam!
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Now let's see what happens if we use the lasso penalty, a.k.a the L1 norm, or, if you asked me,
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I'd call it the absolute value penalty.
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Unfortunately, no one asked me.
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Again, the thick blue line represents lambda equals zero, so there is no extra penalty.
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This is because when we plug lambda equals zero into the equation, the penalty becomes zero.
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And we are left with the original sum of the squared residuals.
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Note, just like before, we'll keep track of the best fitting line plus penalty in this graph on the left.
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The thick orange line represents lambda equals 10, so now we are turning on the penalty and shrinking the slope.
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When lambda equals 10, we start to see a kink in the curve where the slope is 0.
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The thick green line represents lambda equals 20, and this kink at zero is becoming more prominent.
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Lastly, the thick purple line represents lambda equals 40.
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And now the kink at zero is super obvious.
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Now the lowest point in the purple curve,
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aka the optimal slope given the absolute value penalty when lambda equals 40, is zero.
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And that means the slope of the optimal line is 0.
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And that means when lambda equals 40, we ignore weight as a variable when predicting height.
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Double bam!
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In summary, when we increase the ridge regression penalty, a.k.a the L2 penalty, a.k.a the square penalty,
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the optimal slopes shift towards zero, but we retain a nice parabola shape.
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And even when we set lambda to something crazy high like 400, we still end up with an optimal value greater than zero.
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In contrast, when we increase the lasso penalty, a.k.a the L1 penalty, a.k.a the absolute value penalty,
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The optimal value shifts towards zero, but since we have a kink at zero, zero ends up being the optimal slope.
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Bam!
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Hooray!
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We've made it to the end of another exciting StatQuest.
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If you like this StatQuest and want to see more, please subscribe.
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And if you want to support StatQuest, consider contributing to my Patreon campaign,
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becoming a channel member, buying one or two of my original songs or a t-shirt or a hoodie or just donate.
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The links are in the description below.
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All right, until next time, quest on!
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Vocabulary and speaking notes for this lesson
This speaking lesson is built on the video “Ridge vs Lasso Regression, Visualized!!!”. The speaker keeps coming back to these words: slope, penalty, lambda, equal, zero. This video has 86 sentences and 1111 words to shadow. The speech runs for 9:04. The speaker talks at a steady 122 words per minute, a comfortable pace for shadowing. Only 70% of the words are among the 3,000 most common in English, so the vocabulary is demanding.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| slope noun | /sloʊp/ | A surface that either inclines or declines in a regular manner from the perspective of an observer. |
| penalty noun | /ˈpɛn.əl.ti/ | A legal sentence. |
| residual adjective | /ɹɪˈzɪd͡ʒ.u.əl/ | Of, relating to, or remaining as a residue; left over. |
| optimal adjective | /ˈɑptɪməl/ | The best, most favourable or desirable, especially under some restriction. |
| thick adjective | /θɪk/ | Relatively great in extent from one surface to the opposite in its smallest solid dimension. |
| ridge noun | /ɹɪd͡ʒ/ | The back of any animal; especially the upper or projecting part of the back of a quadruped. |
| horizontal adjective | /ˌhoɹəˈzɑn.təl/ | Perpendicular to the vertical; parallel to the plane of the horizon; level, flat. |
| calculate verb | /ˈkælkjʊleɪt/ | To determine the value of something or the solution to something by a mathematical process. |
| graph noun | /ɡɹæf/ | A data chart (graphical representation of data) intended to illustrate the relationship between a set (or sets) of numbers (quantities, measurements or… |
| curve noun | /kɚv/ | A bend, such as in a road. |
| purple noun | /ˈpɝ.pl̩/ | A colour between red and blue; violet, though often closer to magenta. |
| plug noun | /plʌɡ/ | A pronged connecting device which fits into a mating socket, especially an electrical one. |
| compare verb | /kəmˈpɛɚ/ | To assess the similarities and differences between two or more things [“to compare X with Y”]. Having made the comparison of X with Y, one might have found it… |
| versus | /ˈvɝsəs/ | Against; in opposition to. |
| quest noun | /kwɛst/ | A journey or effort in pursuit of a goal (often lengthy, ambitious, or fervent); a mission. |
Phrasal verbs you will hear
| Word | Meaning |
|---|---|
| check out verb | To record the departure or withdrawal of someone or something (such as guests, employees, books, etc.). |
| end up verb | To bring to a conclusion. |
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Passive voice be + past participle — the focus is on what happens, not who does it | be visualized · should be called · are left |
| Present perfect have/has + past participle — a past action that still matters now | has shrunk · We've made |
Pronunciation to watch
The speaker uses 8 contractions and reduced forms, such as we'll, we're, I'd. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: equation /ɪˈkweɪ.ʒən/, contrast /kənˈt(ʃ)ɹæst/, measurement /ˈmɛʒ.ə.mənt/, regression /ɹiːˈɡɹɛʃ.ən/, visualize /ˈvɪʒuəˌlaɪz/
- Long words — get the stress right: residual /ɹɪˈzɪd͡ʒ.u.əl/, horizontal /ˌhoɹəˈzɑn.təl/, parabola /pəˈɹæb.ə.lə/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Shadow it sentence by sentence at normal speed, repeating each one until your rhythm matches the speaker.
- Record yourself and compare with the original, paying attention to words like slope, penalty, residual.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.