Shadowing Practice: Naive Bayes, Clearly Explained!!! - Learn English Speaking with Video
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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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Vocabulary and speaking notes for this lesson
This speaking lesson is built on the video “Naive Bayes, Clearly Explained!!!”. The speaker keeps coming back to these words: message, word, normal, spam, probability. This video has 156 sentences and 1966 words to shadow. The speech runs for 15:11. The speaker talks at a steady 130 words per minute, a comfortable pace for shadowing. 83% of the words are among the 3,000 most common in English; the rest is worth studying before you start.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| spam noun | /spæm/ | Any undesired electronic content automatically generated for commercial purposes. |
| probability noun | /ˌpɹɑ.bəˈbɪl.ə.ti/ | The state of being probable. |
| naive adjective | /naɪˈiv/ | Lacking worldly experience, wisdom, or judgement; unsophisticated. |
| calculate verb | /ˈkælkjʊleɪt/ | To determine the value of something or the solution to something by a mathematical process. |
| occur verb | /əˈkɜː/ | To happen or take place. |
| mathematics noun | /mæθ(.ə)ˈmæt.ɪks/ | An abstract representational system studying numbers, shapes, structures, quantitative change and relationships between them. |
| quest noun | /kwɛst/ | A journey or effort in pursuit of a goal (often lengthy, ambitious, or fervent); a mission. |
| divide verb | /dɪˈvaɪd/ | To split or separate (something) into two or more parts. |
| ignore verb | /ɪɡˈnɔː/ | To deliberately not listen or pay attention to. |
| regardless adverb | /ɹɪˈɡɑɹd.lɪs/ | Without attention to warnings or indications of bad consequences. |
| phrase noun | /ˈfɹeɪ̯z/ | A short written or spoken expression. |
| classification noun | /ˌklæsɪfɪˈkeɪʃən/ | The act of forming into a class or classes; a distribution into groups, as classes, orders, families, etc., according to some common relations or attributes. |
| plug noun | /plʌɡ/ | A pronged connecting device which fits into a mating socket, especially an electrical one. |
| deer noun | /dɪɹ/ | A ruminant mammal with hooves and often antlers, of the family Cervidae, or one of several similar animals from related families of the order Artiodactyla… |
| likewise adverb | /ˈlaɪkˌwaɪz/ | In a similar manner. |
Phrasal verbs you will hear
| Word | Pronunciation | Meaning |
|---|---|---|
| check out verb | To record the departure or withdrawal of someone or something (such as guests, employees, books, etc.). | |
| follow up verb | To take further actions remaining after an event; to continue, revisit, or persist; especially, to maintain communication or verify. | |
| work out verb | /ˌwɝk ˈaʊt/ | To calculate. |
| end up verb | To bring to a conclusion. | |
| take up verb | To pick up. | |
| work around verb | To bypass or circumvent (a problem or obstacle). |
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Passive voice be + past participle — the focus is on what happens, not who does it | is sponsored · are also called · is estimated |
| Present perfect have/has + past participle — a past action that still matters now | have calculated · we've done · We've made |
Pronunciation to watch
The speaker uses 18 contractions and reduced forms, such as don't, I'm, doesn't. Say them the short way, as you hear them.
- The “th” sounds: mathematics /mæθ(.ə)ˈmæt.ɪks/, algorithm /ˈælɡəɹɪðm̩/
- The “sh” and “zh” sounds: classification /ˌklæsɪfɪˈkeɪʃən/, promotion /pɹəˈmoʊʃn̩/, examination /ɪɡˌzæm.ɪˈneɪ.ʃən/, calculation /ˌkælkjuˈleɪʃn̩/, shameless /ˈʃeɪ̯mlɪs/
- Long words — get the stress right: probability /ˌpɹɑ.bəˈbɪl.ə.ti/, mathematics /mæθ(.ə)ˈmæt.ɪks/, classification /ˌklæsɪfɪˈkeɪʃən/, technically /ˈtɛk.nɪˌkəl.i/, terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Shadow it sentence by sentence at normal speed, repeating each one until your rhythm matches the speaker.
- Record yourself and compare with the original, paying attention to words like spam, probability, naive.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.