Prática de Shadowing: Naive Bayes, Clearly Explained!!! - Aprenda a falar inglês com vídeo
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I'm at home during lockdown, working on my StatQuest, yeah!
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I'm at home during lockdown, working on my StatQuest, yeah!
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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 Naive Bays and it's going to be clearly explained.
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This StatQuest is sponsored by JadBio.
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Just add data and their automatic machine learning algorithms will do the rest of the work for you.
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For more details, follow the link in the pinned comment below.
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Note, when most people want to learn about Naive Bayes, they want to learn about the multinomial
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Naive Bayes classifier, and that's what we talk about in this video.
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However, just know that there is another commonly used version of Naive Bayes called Gaussian Naive Bayes Classification,
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and I cover that in a follow -up stat quest.
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So check that one out when you're done with this quest.
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Bam.
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Now, imagine we received normal messages from friends and family.
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And we also received spam, unwanted messages that are usually scams or unsolicited advertisements.
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And we wanted to filter out the spam messages. So, the first thing we do is make a histogram of all the words
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that occur in the normal messages from friends and family.
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We can use the histogram to calculate the probabilities of seeing each word given that it was in a normal message.
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For example, the probability we see the word deer, given that we saw it in a normal message,
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is 8, the total number of times dear occurred in normal messages,
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divided by 17, the total number of words in all of the normal messages.
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And that gives us 0 .47.
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So let's put that over the word dear so we don't forget it.
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Likewise, the probability that we see the word friend, given that we saw it in a normal message,
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is 5, the total number of times friend occurred in normal messages,
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divided by 17, the total number of words in all of the normal messages.
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And that gives us 0 .29.
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So let's put that over the word friend so we don't forget it.
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Likewise, the probability that we see the word lunch, given that it is in a normal message, is 0 .18.
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And the probability that we see the word money, given that it is in a normal message, is 0 .06.
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Now we make a histogram of all the words that occur in the spam.
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And calculate the probability of seeing the word deer, given that we saw it in the spam.
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And that is 2, the number of times we saw deer in the spam, divided by 7, the total number of words in the spam.
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And that gives us 0 .29.
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Likewise, we calculate the probability of seeing the remaining words, given that they were in the spam.
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Bam!
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Now, because these histograms are taking up a lot of space, let's get rid of them, but keep the probabilities.
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Oh no!
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It's the dreaded terminology alert!
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Because we have calculated the probabilities of discrete, individual words, and not the probability of something continuous,
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like weight or height, these probabilities are also called likelihoods.
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I mention this because some tutorials say these are probabilities, and others say they are likelihoods.
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In this case, the terms are interchangeable, so don't sweat it.
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We'll talk more about probabilities versus likelihoods when we talk about Gaussian naive bays in the follow -up quest.
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Now, imagine we got a new message that said,
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Dear Friend, and we want to decide if it is a normal message or spam down.
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We start with an initial guess about the probability that any message, regardless of what it says, is a normal message.
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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 12 messages are normal messages, our initial guess will be 0 .67.
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So let's put that under the normal messages so we don't forget it.
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Oh no!
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It's another dreaded terminology alert!
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The initial guess that we observe a normal message is called a prior probability.
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Now we multiply the initial guess by the probability that the word dear occurs in a normal message,
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and the probability that the word friend occurs in a normal message.
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Now we just plug in the values that we worked out earlier and do the math.
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Beep, boop, beep, boop, beep.
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And we get 0 .09.
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We can think of 0 .09 as the score that
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Dear Friend gets if it is a normal message.
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However, technically, it is proportional to the probability that the message is normal, given that it says Dear Friend.
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So let's put that on top of the normal messages so we don't forget.
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Now, just like we did before, we start with an initial guess about the probability that any message, regardless of what it says, is spam.
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And just like before, the guess can be any probability we want, but a common guess is estimated from the training data.
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And since 4 of the 12 messages are spam, our initial guess will be 0 .33.
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So let's put that under the spam so we don't forget it.
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Now we multiply that initial guess by the probability that the word dear occurs in spam.
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And the probability that the word friend occurs in spam.
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Now we just plugged in the values that we worked out earlier and do the math.
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Bip, boop, bip, boop, bip.
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And we get 0 .01.
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Like before, we can think of 0 .01 as the score that
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Dear Friend gets if it is spam.
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However, technically, it is proportional to the probability that the message is spam given that it says Dear Friend.
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And because the score we got for normal message, 0 .09, is greater than the score we got for spam,
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0 .01, we will decide that
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Dear Friend is a normal message.
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Double bam!
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Now, before we move on to a slightly more complex situation, let's review what we've done so far.
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We started with histograms of all the words in the normal messages, and all of the words in the spam.
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Then we calculated the probabilities of seeing each word, given that we saw the word in either a normal message or spam.
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Then we made an initial guess about the probability of seeing a normal message.
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This guess can be anything between 0 and 1, but we based ours on the classifications in the training dataset.
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Then we made the same sort of guess about the probability of seeing spam.
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Then we multiplied our initial guess
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that the message was normal by the probabilities of seeing the words dear and friend given that the message was normal.
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Then we multiplied our initial guess that the message was spam by the probabilities of seeing the words
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Dear and Friend given that the message was spam.
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Then we did the math and decided that
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Dear Friend was a normal message because 0 .09 is greater than 0 .01.
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Now that we understand the basics of how
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Naive Bayes classification works, let's look at a slightly more complicated example.
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This time, let's try to classify this message, lunch, money, money, money, money.
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Note, this message contains the word money four times.
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And since the probability of seeing the word money is much higher in spam than in normal messages,
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then it seems reasonable to predict that this message will end up being spam.
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So let's do the math math.
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Calculating the score for a normal message works just like before.
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We start with the initial guess, then we multiply it by the probability we see lunch, given that it is in a normal message,
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and the probability we see money four times, given that it is in a normal message.
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When we do the math, we get this tiny number.
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However, when we do the same calculation for spam, we get zero.
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This is because the probability we see lunch in spam is zero since it was not in the training data.
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And when we plug in zero for the probability we see lunch given that it was in spam,
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then it doesn't matter what value we picked for the initial guess that the message was spam,
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and it doesn't matter what the probability is that we see money given that the message was spam.
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because anything times zero is zero.
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In other words, if a message contains the word lunch, it will not be classified as spam.
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And that means we will always classify the messages with lunch in them as normal,
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no matter how many times we see the word money. And that's a problem.
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To work around this problem, people usually add one count, represented by a black box, to each word in the histograms.
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The number of counts we add to each word is typically referred to with the Greek letter alpha.
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In this case, alpha equals 1, but we could have set it to anything.
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Anyway, now when we calculate the probabilities of observing each word, we never get zero.
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For example, the probability of seeing lunch, given that it is in spam, is 1 divided by 7,
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the total number of words in spam, plus 4, the extra counts that we added.
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And that gives us 0 .09.
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adding counts to each word does not change our initial guess that a message is normal, or the initial guess that the message is spam,
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because adding a count to each word did not change the number of messages in the training data set that are normal,
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or the number of messages that are spam.
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Now when we calculate the scores for this message, we still get a small number for the normal message.
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But now when we calculate the value for spam, we get a value greater than zero.
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And since the value for spam is greater than the one for a normal message, we classify the message as spam.
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Spam!
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Now let's talk about why
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Naive Bayes is naive.
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The thing that makes
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Naive Bayes so naive is that it treats all word orders the same.
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For example, the normal message score for the phrase
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Dear Friend is the exact same for the score for Friend Dear.
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In other words, regardless of how the words are ordered, we get 0 .08.
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Treating all word orders equal is very different from how you and I communicate.
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Every language has grammar rules and common phrases, but Naive Bayes ignores all of that stuff.
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Instead, Naive Bayes treats language like it is just a bag full of words and each message is a random handful of them.
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Naive Bayes ignores all the rules because keeping track of every single reasonable phrase in a language would be impossible.
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That said, even though Naive Bayes is naive, it tends to perform surprisingly well when separating normal messages from spam.
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In machine learning lingo, we'd say that by ignoring relationships among words, Naive Bayes has high bias.
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But, because it works well in practice, Naive Bayes has low variance.
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Shameless Self -Promotion.
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If you are not already familiar with the terms bias and variance, check out the quest.
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The link is in the description below.
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Triple spam!
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Oh no, it's one last shameless self -promotion.
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One awesome way to support StatQuest is to purchase the 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 eight 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 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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Alright, until next time, Quest on!
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Vocabulário e dicas de fala para esta lição
Esta aula de conversação usa o vídeo “Naive Bayes, Clearly Explained!!!”. As palavras que mais se repetem: message, word, normal, spam, probability. Este vídeo tem 156 frases e 1966 palavras para praticar shadowing. A fala dura 15:11. O falante mantém um ritmo estável de cerca de 130 palavras por minuto, confortável para o shadowing. 83% das palavras estão entre as 3.000 mais comuns do inglês; vale a pena estudar o restante antes de começar.
Vocabulário principal deste vídeo
15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| spam substantivo | /spæm/ | spam, lixo eletrônico |
| probability substantivo | /ˌpɹɑ.bəˈbɪl.ə.ti/ | probabilidade |
| naive adjetivo | /naɪˈiv/ | ingénuo, ingênuo |
| calculate verbo | /ˈkælkjʊleɪt/ | calcular |
| occur verbo | /əˈkɜː/ | ocorrer, acontecer |
| mathematics substantivo | /mæθ(.ə)ˈmæt.ɪks/ | matemática |
| quest substantivo | /kwɛst/ | busca |
| divide verbo | /dɪˈvaɪd/ | dividir, separar |
| ignore verbo | /ɪɡˈnɔː/ | ignorar |
| phrase substantivo | /ˈfɹeɪ̯z/ | expressão, frase |
| classification substantivo | /ˌklæsɪfɪˈkeɪʃən/ | classificação |
| plug substantivo | /plʌɡ/ | plugue |
| deer substantivo | /dɪɹ/ | veado, cervo |
| likewise advérbio | /ˈlaɪkˌwaɪz/ | similarmente, igualmente |
| likelihood substantivo | /ˈlaɪklihʊd/ | probabilidade |
Phrasal verbs que você vai ouvir
| Palavra | Pronúncia | Significado |
|---|---|---|
| check out verbo | verificar, sacar só | |
| work out verbo | /ˌwɝk ˈaʊt/ | calcular |
| end up verbo | terminar, acabar |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | is sponsored · are also called · is estimated |
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | have calculated · we've done · We've made |
Pronúncia para ficar de olho
O falante usa 18 contrações e formas reduzidas, como don't, I'm, doesn't. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “th”: mathematics /mæθ(.ə)ˈmæt.ɪks/, algorithm /ˈælɡəɹɪðm̩/
- Os sons de “sh” e “zh”: classification /ˌklæsɪfɪˈkeɪʃən/, promotion /pɹəˈmoʊʃn̩/, examination /ɪɡˌzæm.ɪˈneɪ.ʃən/, calculation /ˌkælkjuˈleɪʃn̩/, shameless /ˈʃeɪ̯mlɪs/
- Palavras longas — acerte a sílaba tônica: probability /ˌpɹɑ.bəˈbɪl.ə.ti/, mathematics /mæθ(.ə)ˈmæt.ɪks/, classification /ˌklæsɪfɪˈkeɪʃən/, technically /ˈtɛk.nɪˌkəl.i/, terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: calculate /ˈkælkjʊleɪt/, quest /kwɛst/, divide /dɪˈvaɪd/, plug /plʌɡ/, likelihood /ˈlaɪklihʊd/
- /l/ final — a língua toca o céu da boca, não vira “u”: triple /ˈtɹɪp.əl/, handful /ˈhæn(d)fʊl/, tutorial /ˌtjuːˈtɔːɹɪəl/
- /h/ — um sopro suave, diferente do “r”: likelihood /ˈlaɪklihʊd/, handful /ˈhæn(d)fʊl/, histogram /ˈhɪstəˌɡɹæm/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Faça shadowing frase por frase na velocidade normal, repetindo cada uma até o seu ritmo ficar igual ao do falante.
- Grave a sua voz e compare com o original, prestando atenção a palavras como spam, probability, naive.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.