Pratique du Shadowing: Gaussian Naive Bayes, Clearly Explained!!! - Apprendre l'anglais à l'oral avec la vidéo
Création de la leçon...
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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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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale de niveau C1 s’appuie sur la vidéo « Gaussian Naive Bayes, Clearly Explained!!! ». Les mots qui reviennent le plus souvent : Troll, log, likelihood, candy, boop. Cette vidéo contient 107 phrases et 1118 mots à répéter en shadowing. La partie parlée dure 9:25. Le locuteur parle à un rythme régulier d’environ 119 mots par minute, confortable pour le shadowing. Seuls 77 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.
Vocabulaire clé de cette vidéo
Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| likelihood nom | /ˈlaɪklihʊd/ | vraisemblance |
| popcorn nom | /ˈpɑp.kɔɹn/ | popcorn, maïs soufflé |
| soda nom | /ˈsoʊdə/ | carbonate de soude, soude |
| beep nom | /biːp/ | pouet |
| gram nom | /ˈɡɹæm/ | gramme |
| deviation nom | /ˌdiː.viˈeɪʃən/ | déviation |
| classify verbe | /ˈklæs.əˌfaɪ/ | classifier |
| correspond verbe | /ˌkoɹəˈspɑnd/ | correspondre (...à qqchose) |
| probability nom | /ˌpɹɑ.bəˈbɪl.ə.ti/ | probabilité |
| multiplication nom | /ˌmʌltɪplɪˈkeɪʃən/ | multiplication |
| shameless adjectif | /ˈʃeɪ̯mlɪs/ | effronté, éhonté |
| validation nom | /ˌvæl.əˈdeɪ.ʃən/ | validation |
| coordinate verbe | /koʊˈɔɹ.dəˌneɪt/ | coordonner |
| calculate verbe | /ˈkælkjʊleɪt/ | calculer |
| stat adverbe | /stæt/ | maintenant, là |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| check out verbe | mater, regarder |
| show up verbe | apparaître, se montrer se pointer |
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | people who love · people who do · someone who does |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | is named · is estimated · are called |
Prononciation à surveiller
Le locuteur utilise 9 contractions et formes réduites, comme don't, doesn't, I'm. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: deviation /ˌdiː.viˈeɪʃən/, multiplication /ˌmʌltɪplɪˈkeɪʃən/, shameless /ˈʃeɪ̯mlɪs/, validation /ˌvæl.əˈdeɪ.ʃən/
- Mots longs — placez bien l’accent: deviation /ˌdiː.viˈeɪʃən/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, multiplication /ˌmʌltɪplɪˈkeɪʃən/, validation /ˌvæl.əˈdeɪ.ʃən/, coordinate /koʊˈɔɹ.dəˌneɪt/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Répétez phrase par phrase à vitesse normale, en reprenant chacune jusqu’à ce que votre rythme corresponde à celui du locuteur.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme likelihood, popcorn, soda.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.