शैडोइंग अभ्यास: Gaussian Naive Bayes, Clearly Explained!!! - वीडियो के साथ अंग्रेजी बोलना सीखें
पाठ बनाया जा रहा है...
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Beep boop boop beep boop boop beep boop.
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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 Gaussian Naive Bays, and it's going to be clearly explained.
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Note.
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This stat quest assumes that you are already familiar with the main ideas behind multinomial Naive Bays.
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If not, check out the quest!
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The link is in the description below.
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This StatQuest also assumes that you are familiar with the log function, the normal or Gaussian distribution,
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and the difference between probability and likelihood.
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If not, check out the quests.
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The links are in the description below.
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Imagine we wanted to predict if someone would love the 1990 movie Troll 2 or not.
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So we collected data from people that love Troll 2, and from people that do not love Troll 2.
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We measured the amount of popcorn they ate each day.
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How much soda pop they drank.
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and how much candy they ate.
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The mean for popcorn for the people who love Troll 2 is 24.
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and the standard deviation is 4.
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And a Gaussian, or normal distribution, with mean equals 24 and standard deviation equals 4 looks like this.
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Likewise, the average amount of popcorn for people who do not love Troll 2 is 4.
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and the standard deviation is 2.
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And that corresponds to this Gaussian, or normal distribution.
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Now we calculate the mean and standard deviation for Soda Pop for people that love Troll 2.
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and draw the corresponding Gaussian distribution.
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Then we do the same thing for the people that do not love Troll 2.
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Lastly, we draw the Gaussian distributions for candy.
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Gaussian Naive Bayes is named after the Gaussian distributions that represent the data in the training dataset.
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Now someone new shows up.
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and says they eat 20 grams of popcorn.
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and drink 500 milliliters of soda pop, and eat 25 grams of candy every day.
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Let's use Gaussian Naive Bayes to decide if they love Troll 2 or not.
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The first thing we do is make an initial guess that they love Troll 2.
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This guess can be any probability that we want, but a common guess is estimated from the training data.
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For example, since 8 of the 16 people in the training data loved Troll 2, the initial guess will be 0 .5.
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So we'll put that up here so we don't forget.
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Likewise, the initial guess for Does Not Love Troll 2 is 0 .5.
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So let's put that here so we don't forget.
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Oh no, it's the dreaded terminology alert.
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The initial guesses are called prior probabilities.
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Now, the score for Love's Troll 2 is...
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The initial guess that the person loves Troll 2, times the likelihood that they eat 10 grams of popcorn given that they love Troll 2.
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Note: the likelihood is the y -axis coordinate on the curve that corresponds to the x -axis coordinate.
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and we multiply that by the likelihood that they drink 500 milliliters of soda pop given that they love Troll 2.
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times the likelihood that they eat 25 grams of candy given that they love Troll 2.
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The initial guess that someone loves Troll 2 is 0 .5.
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The likelihood for popcorn is 0 .06.
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The likelihood for soda pop is 0 .004, And the likelihood for candy is...
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A really, really small number.
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When we get really, really small numbers, it's a good idea to take the log of everything to prevent something called underflow.
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The general idea of Underflow is:
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Every computer has a limit to how close a number can
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get to zero before it can no longer accurately keep track of that number.
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When a number gets smaller than that limit, we run into underflow problems and errors occur.
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So we use the log function to avoid underflow.
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Any log will do, but the natural log, or log base E, is the most commonly used log in statistics and machine learning.
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So we take the log of everything, and the log turns the multiplication into the sum of the individual logs.
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The log base E of 0 .5 is...
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-0 .69 The log of 0 .06 is negative 2 .8.
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The log of 0 .004 is -5 .5.
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And the log of this really, really small number is negative 115.
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Now we just add this up.
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And we get negative 124.
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So the log of the Love's Troll 2 score is negative 124.
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BAM!
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Now let's calculate the score for Not Loving Troll 2.
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We start with the initial guess that someone does not love Troll 2.
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times the likelihood that they eat 20 grams of popcorn given that they do not love Troll 2.
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times the likelihood that they drink 500 milliliters of soda pop, times the likelihood that they eat 25 grams of candy.
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So let's plug in the numbers.
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Beep, boop, beep.
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Boop.
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and take the log of everything.
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and that turns the multiplication into the sum of logs.
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Now we just do the math.
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Beep, boop, boop, boop.
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and we get -48.
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And since the score for Does Not Love Troll 2 is greater than the score for Loves Troll 2,
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We will classify this person as someone who does not love Troll 2.
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Double bam!
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Note: When we look at the raw data, it almost looks like we should have classified this person as someone who loves Troll 2.
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After all, they ate a lot more popcorn than the average person who doesn't love Troll 2.
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and they drank as much soda as the average person who loves Troll 2.
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However, the big thing is that they ate a lot more candy than the people who loved Troll 2.
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and the log of the likelihoods for candy are way different.
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And this difference is what made us classify the new person as someone who does not love Troll 2.
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In other words, Candy can have a much larger say in whether
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or not someone loves Troll 2 than popcorn and soda pop.
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And this means we might only need candy to make classifications.
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We can use cross -validation to help us decide which things, popcorn, soda pop, and /or candy, help us make the best classifications.
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Shameless self -promotion.
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If you don't already know about cross -validation, check out the quest.
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The link is in the description below.
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Triple bam.
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Oh no, it's another shameless self -promotion.
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One awesome way to support StatQuest is to purchase the Gaussia Naive Bayes StatQuest Study Guide.
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It has everything you need to study for an exam or job interview.
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It's seven pages of total awesomeness.
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And while you're there, check out the other StatQuest study guides.
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There's something for everyone!
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Hooray!
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We've made it to the end of another exciting stat quest!
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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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Alright, until next time, quest on!
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इस पाठ की शब्दावली और बोलने के सुझाव
यह C1 स्तर का स्पीकिंग पाठ वीडियो “Gaussian Naive Bayes, Clearly Explained!!!” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: Troll, log, likelihood, candy, boop। इस वीडियो में शैडोइंग के लिए 107 वाक्य और 1118 शब्द हैं। बोला गया हिस्सा 9:25 लंबा है। वक्ता लगभग 119 शब्द प्रति मिनट की स्थिर गति से बोलता है, जो शैडोइंग के लिए आरामदायक है। केवल 77% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।
इस वीडियो की मुख्य शब्दावली
वीडियो के 12 सबसे कठिन शब्द, उच्चारण और अर्थ के साथ:
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| popcorn संज्ञा | /ˈpɑp.kɔɹn/ | पॉपकॉर्न |
| gram संज्ञा | /ˈɡɹæm/ | ग्राम |
| probability संज्ञा | /ˌpɹɑ.bəˈbɪl.ə.ti/ | प्रायिकता, संभावना |
| multiplication संज्ञा | /ˌmʌltɪplɪˈkeɪʃən/ | गुणा |
| shameless विशेषण | /ˈʃeɪ̯mlɪs/ | निर्लज्ज, बेशर्म |
| calculate क्रिया | /ˈkælkjʊleɪt/ | गणना करना, हिसाब रखना |
| axis संज्ञा | /ˈæksɪs/ | धुरी, अक्ष |
| excite क्रिया | /ɪkˈsaɪt/ | उत्तेजित करना, उत्साहित करना |
| multiply क्रिया | /ˈmʌltɪplaɪ/ | गुणा करना |
| dread संज्ञा | /dɹɛd/ | वहशत, सहम |
| terminology संज्ञा | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | शब्दावली |
| donate क्रिया | /ˈdoʊˌneɪt/ | दान करना, दान देना |
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | people who love · people who do · someone who does |
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | is named · is estimated · are called |
ध्यान देने योग्य उच्चारण
वक्ता 9 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे don't, doesn't, I'm। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “sh” और “zh” की ध्वनियाँ: deviation /ˌdiː.viˈeɪʃən/, multiplication /ˌmʌltɪplɪˈkeɪʃən/, shameless /ˈʃeɪ̯mlɪs/, validation /ˌvæl.əˈdeɪ.ʃən/
- लंबे शब्द — बलाघात सही रखें: deviation /ˌdiː.viˈeɪʃən/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, multiplication /ˌmʌltɪplɪˈkeɪʃən/, validation /ˌvæl.əˈdeɪ.ʃən/, coordinate /koʊˈɔɹ.dəˌneɪt/
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 popcorn, gram, probability 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।