शैडोइंग अभ्यास: Ridge vs Lasso Regression, Visualized!!! - वीडियो के साथ अंग्रेजी बोलना सीखें
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86 वाक्य
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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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इस पाठ की शब्दावली और बोलने के सुझाव
यह स्पीकिंग पाठ वीडियो “Ridge vs Lasso Regression, Visualized!!!” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: slope, penalty, lambda, equal, zero। इस वीडियो में शैडोइंग के लिए 86 वाक्य और 1111 शब्द हैं। बोला गया हिस्सा 9:04 लंबा है। वक्ता लगभग 122 शब्द प्रति मिनट की स्थिर गति से बोलता है, जो शैडोइंग के लिए आरामदायक है। केवल 70% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| slope संज्ञा | /sloʊp/ | ढाल |
| penalty संज्ञा | /ˈpɛn.əl.ti/ | दण्ड, सज़ा |
| thick विशेषण | /θɪk/ | मोटा |
| ridge संज्ञा | /ɹɪd͡ʒ/ | कटक, डाँग |
| horizontal विशेषण | /ˌhoɹəˈzɑn.təl/ | क्षैतिज |
| calculate क्रिया | /ˈkælkjʊleɪt/ | गणना करना, हिसाब रखना |
| graph संज्ञा | /ɡɹæf/ | लेखाचित्र, ग्राफ़ |
| curve संज्ञा | /kɚv/ | चक्कर, घुमाव |
| purple संज्ञा | /ˈpɝ.pl̩/ | जामुनी |
| compare क्रिया | /kəmˈpɛɚ/ | तौलना |
| quest संज्ञा | /kwɛst/ | खोज |
| axis संज्ञा | /ˈæksɪs/ | धुरी, अक्ष |
| equation संज्ञा | /ɪˈkweɪ.ʒən/ | समीकरण |
| ignore क्रिया | /ɪɡˈnɔː/ | उपेक्षा, उपेक्षा करना |
| summary संज्ञा | /ˈsʌm.ə.ɹi/ | सारांश, ख़ुलासा |
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | be visualized · should be called · are left |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | has shrunk · We've made |
ध्यान देने योग्य उच्चारण
वक्ता 8 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे we'll, we're, I'd। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “sh” और “zh” की ध्वनियाँ: equation /ɪˈkweɪ.ʒən/, contrast /kənˈt(ʃ)ɹæst/, measurement /ˈmɛʒ.ə.mənt/, regression /ɹiːˈɡɹɛʃ.ən/, visualize /ˈvɪʒuəˌlaɪz/
- लंबे शब्द — बलाघात सही रखें: residual /ɹɪˈzɪd͡ʒ.u.əl/, horizontal /ˌhoɹəˈzɑn.təl/, parabola /pəˈɹæb.ə.lə/
हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:
- /v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: curve /kɚv/, versus /ˈvɝsəs/, quest /kwɛst/, equation /ɪˈkweɪ.ʒən/, variable [ˈvæɹ.i.ə.bl̩]
- /z/ — इसे “ज” (/dʒ/) न बनाएँ: residual /ɹɪˈzɪd͡ʒ.u.əl/, horizontal /ˌhoɹəˈzɑn.təl/, observe /əbˈzɜːv/, likewise /ˈlaɪkˌwaɪz/, visualize /ˈvɪʒuəˌlaɪz/
- /æ/ — “ऐ” से ज़्यादा खुला: calculate /ˈkælkjʊleɪt/, graph /ɡɹæf/, axis /ˈæksɪs/, contrast /kənˈt(ʃ)ɹæst/, variable [ˈvæɹ.i.ə.bl̩]
इस वीडियो से अभ्यास कैसे करें
- पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
- सामान्य गति पर एक-एक वाक्य की शैडोइंग करें और हर वाक्य तब तक दोहराएँ जब तक आपकी लय वक्ता से न मिल जाए।
- अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर slope, penalty, thick जैसे शब्दों पर ध्यान दें।
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।