Shadowing-Übung: Gaussian Naive Bayes, Clearly Explained!!! - Englisch Sprechen Lernen mit Video
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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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Wortschatz und Sprechhinweise zu dieser Lektion
Diese Sprechübung auf Niveau C1 basiert auf dem Video „Gaussian Naive Bayes, Clearly Explained!!!“. Diese Wörter kommen am häufigsten vor: Troll, log, likelihood, candy, boop. Dieses Video enthält 107 Sätze und 1118 Wörter zum Nachsprechen. Der gesprochene Teil dauert 9:25. Der Sprecher spricht gleichmäßig mit etwa 119 Wörtern pro Minute – ein angenehmes Tempo fürs Shadowing. Nur 77 % der Wörter gehören zu den 3.000 häufigsten im Englischen, der Wortschatz ist also anspruchsvoll.
Wichtiger Wortschatz in diesem Video
Die 15 anspruchsvollsten Wörter aus dem Video, mit Aussprache und Bedeutung:
| Wort | Aussprache | Bedeutung |
|---|---|---|
| likelihood Substantiv | /ˈlaɪklihʊd/ | Wahrscheinlichkeit |
| popcorn Substantiv | /ˈpɑp.kɔɹn/ | Popcorn, Puffmais |
| soda Substantiv | /ˈsoʊdə/ | Soda |
| beep Substantiv | /biːp/ | Hupen, Tuten |
| gram Substantiv | /ˈɡɹæm/ | Gramm |
| deviation Substantiv | /ˌdiː.viˈeɪʃən/ | Abweichung, Abweichen |
| classify Verb | /ˈklæs.əˌfaɪ/ | einordnen, einstufen |
| correspond Verb | /ˌkoɹəˈspɑnd/ | entsprechen, korrespondieren |
| probability Substantiv | /ˌpɹɑ.bəˈbɪl.ə.ti/ | Wahrscheinlichkeit |
| multiplication Substantiv | /ˌmʌltɪplɪˈkeɪʃən/ | Multiplikation |
| shameless Adjektiv | /ˈʃeɪ̯mlɪs/ | schamlos, unverschämt |
| validation Substantiv | /ˌvæl.əˈdeɪ.ʃən/ | Bestätigung, Gültigkeitserklärung |
| coordinate Verb | /koʊˈɔɹ.dəˌneɪt/ | koordinieren, abstimmen |
| calculate Verb | /ˈkælkjʊleɪt/ | kalkulieren, berechnen |
| axis Substantiv | /ˈæksɪs/ | Achse |
Phrasal Verbs, die du hören wirst
| Wort | Bedeutung |
|---|---|
| check out Verb | überprüfen |
| show up Verb | auftauchen, erscheinen |
Grammatik in diesem Video
Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:
| Struktur | Im Video |
|---|---|
| Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache | people who love · people who do · someone who does |
| Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut | is named · is estimated · are called |
Aussprache, auf die du achten solltest
Der Sprecher verwendet 9 Kurzformen und abgeschwächte Formen, zum Beispiel don't, doesn't, I'm. Sprich sie in der kurzen Form, so wie du sie hörst.
- Die Laute „sh“ und „zh“: deviation /ˌdiː.viˈeɪʃən/, multiplication /ˌmʌltɪplɪˈkeɪʃən/, shameless /ˈʃeɪ̯mlɪs/, validation /ˌvæl.əˈdeɪ.ʃən/
- Lange Wörter – auf die Betonung achten: deviation /ˌdiː.viˈeɪʃən/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, multiplication /ˌmʌltɪplɪˈkeɪʃən/, validation /ˌvæl.əˈdeɪ.ʃən/, coordinate /koʊˈɔɹ.dəˌneɪt/
So übst du mit diesem Video
- Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
- Sprich Satz für Satz in normaler Geschwindigkeit nach und wiederhole jeden, bis dein Rhythmus zum Sprecher passt.
- Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie likelihood, popcorn, soda.
Was ist die Shadowing-Technik?
Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.