跟读练习: Gaussian Naive Bayes, Clearly Explained!!! - 通过视频学习英语口语
正在创建课程...
1
Beep boop boop beep boop boop beep boop.
2
StatQuest.
3
Hello, I'm Josh Starmer and welcome to StatQuest.
4
Today we're going to talk about Gaussian Naive Bays, and it's going to be clearly explained.
5
Note.
6
This stat quest assumes that you are already familiar with the main ideas behind multinomial Naive Bays.
7
If not, check out the quest!
8
The link is in the description below.
9
This StatQuest also assumes that you are familiar with the log function, the normal or Gaussian distribution,
10
and the difference between probability and likelihood.
11
If not, check out the quests.
12
The links are in the description below.
13
Imagine we wanted to predict if someone would love the 1990 movie Troll 2 or not.
14
So we collected data from people that love Troll 2, and from people that do not love Troll 2.
15
We measured the amount of popcorn they ate each day.
16
How much soda pop they drank.
17
and how much candy they ate.
18
The mean for popcorn for the people who love Troll 2 is 24.
19
and the standard deviation is 4.
20
And a Gaussian, or normal distribution, with mean equals 24 and standard deviation equals 4 looks like this.
21
Likewise, the average amount of popcorn for people who do not love Troll 2 is 4.
22
and the standard deviation is 2.
23
And that corresponds to this Gaussian, or normal distribution.
24
Now we calculate the mean and standard deviation for Soda Pop for people that love Troll 2.
25
and draw the corresponding Gaussian distribution.
26
Then we do the same thing for the people that do not love Troll 2.
27
Lastly, we draw the Gaussian distributions for candy.
28
Gaussian Naive Bayes is named after the Gaussian distributions that represent the data in the training dataset.
29
Now someone new shows up.
30
and says they eat 20 grams of popcorn.
31
and drink 500 milliliters of soda pop, and eat 25 grams of candy every day.
32
Let's use Gaussian Naive Bayes to decide if they love Troll 2 or not.
33
The first thing we do is make an initial guess that they love Troll 2.
34
This guess can be any probability that we want, but a common guess is estimated from the training data.
35
For example, since 8 of the 16 people in the training data loved Troll 2, the initial guess will be 0 .5.
36
So we'll put that up here so we don't forget.
37
Likewise, the initial guess for Does Not Love Troll 2 is 0 .5.
38
So let's put that here so we don't forget.
39
Oh no, it's the dreaded terminology alert.
40
The initial guesses are called prior probabilities.
41
Now, the score for Love's Troll 2 is...
42
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.
43
Note: the likelihood is the y -axis coordinate on the curve that corresponds to the x -axis coordinate.
44
and we multiply that by the likelihood that they drink 500 milliliters of soda pop given that they love Troll 2.
45
times the likelihood that they eat 25 grams of candy given that they love Troll 2.
46
The initial guess that someone loves Troll 2 is 0 .5.
47
The likelihood for popcorn is 0 .06.
48
The likelihood for soda pop is 0 .004, And the likelihood for candy is...
49
A really, really small number.
50
When we get really, really small numbers, it's a good idea to take the log of everything to prevent something called underflow.
51
The general idea of Underflow is:
52
Every computer has a limit to how close a number can
53
get to zero before it can no longer accurately keep track of that number.
54
When a number gets smaller than that limit, we run into underflow problems and errors occur.
55
So we use the log function to avoid underflow.
56
Any log will do, but the natural log, or log base E, is the most commonly used log in statistics and machine learning.
57
So we take the log of everything, and the log turns the multiplication into the sum of the individual logs.
58
The log base E of 0 .5 is...
59
-0 .69 The log of 0 .06 is negative 2 .8.
60
The log of 0 .004 is -5 .5.
61
And the log of this really, really small number is negative 115.
62
Now we just add this up.
63
And we get negative 124.
64
So the log of the Love's Troll 2 score is negative 124.
65
BAM!
66
Now let's calculate the score for Not Loving Troll 2.
67
We start with the initial guess that someone does not love Troll 2.
68
times the likelihood that they eat 20 grams of popcorn given that they do not love Troll 2.
69
times the likelihood that they drink 500 milliliters of soda pop, times the likelihood that they eat 25 grams of candy.
70
So let's plug in the numbers.
71
Beep, boop, beep.
72
Boop.
73
and take the log of everything.
74
and that turns the multiplication into the sum of logs.
75
Now we just do the math.
76
Beep, boop, boop, boop.
77
and we get -48.
78
And since the score for Does Not Love Troll 2 is greater than the score for Loves Troll 2,
79
We will classify this person as someone who does not love Troll 2.
80
Double bam!
81
Note: When we look at the raw data, it almost looks like we should have classified this person as someone who loves Troll 2.
82
After all, they ate a lot more popcorn than the average person who doesn't love Troll 2.
83
and they drank as much soda as the average person who loves Troll 2.
84
However, the big thing is that they ate a lot more candy than the people who loved Troll 2.
85
and the log of the likelihoods for candy are way different.
86
And this difference is what made us classify the new person as someone who does not love Troll 2.
87
In other words, Candy can have a much larger say in whether
88
or not someone loves Troll 2 than popcorn and soda pop.
89
And this means we might only need candy to make classifications.
90
We can use cross -validation to help us decide which things, popcorn, soda pop, and /or candy, help us make the best classifications.
91
Shameless self -promotion.
92
If you don't already know about cross -validation, check out the quest.
93
The link is in the description below.
94
Triple bam.
95
Oh no, it's another shameless self -promotion.
96
One awesome way to support StatQuest is to purchase the Gaussia Naive Bayes StatQuest Study Guide.
97
It has everything you need to study for an exam or job interview.
98
It's seven pages of total awesomeness.
99
And while you're there, check out the other StatQuest study guides.
100
There's something for everyone!
101
Hooray!
102
We've made it to the end of another exciting stat quest!
103
If you like this StatQuest and want to see more, please subscribe.
104
And if you want to support StatQuest, consider contributing to my Patreon campaign,
105
becoming a channel member, buying one or two of my original songs or a t -shirt or a hoodie, or just donate.
106
The links are in the description below.
107
Alright, until next time, quest on!
📺 同频道
C11
The GELU, SiLU and SwiGLU activation functions, clearly explained!!!
Business EnglishUniversity StudentsWorking Adults
C11
The Essential Main Ideas of Neural Networks
Business EnglishUniversity StudentsWorking Adults
C11
A Gentle Introduction to Machine Learning
Business EnglishUniversity StudentsWorking Adults
✨ 推荐视频
C111
93 句
Naive Bayes
Business EnglishUniversity StudentsWorking Adults
C12
347 句
How to Read Anyone Instantly – Nietzsche’s 18 Psychological Truths
Business EnglishUniversity StudentsWorking Adults
C11
156 句
SELECT COUNT (*) can impact your Backend Application Performance, here is why
Business EnglishUniversity StudentsWorking Adults
B21
60 句
Game Theory Scene | 21(2008) | Now Playing
Business EnglishUniversity StudentsWorking Adults
C12
405 句
Netflix product manager mock interview and tips (with ex-Netflix Lead PM)
Business EnglishInterview EnglishUniversity Students
C11
227 句
How to Use Google Gemini Al (Full Tutorial)
Business EnglishUniversity StudentsWorking Adults
C11
125 句
Introduction to statistical prediction models
Business EnglishUniversity StudentsWorking Adults
C11
108 句
How I Learned Procedural Generation
Business EnglishUniversity StudentsWorking Adults
C12
86 句
System Design: Why is Kafka Popular?
Business EnglishUniversity StudentsWorking Adults
C12
41 句
Mathematical Statistics and the Theory of Probability in Casinos
Business EnglishUniversity StudentsWorking Adults
关于本课
您正在使用跟读技巧通过视频"Gaussian Naive Bayes, Clearly Explained!!!"练习英语口语和发音。
每天练习15到30分钟，将显著提高您的英语流利度和发音准确度。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。