
Chinese: 
親愛的學者們，歡迎來到兩分鐘論文，我是KárolyZsolnai-Fehér。
決策樹是一個非常良好的工具，協助你從大量數據中做出正確的決策。
這邊有個經典的例子。
從資訊中分辨出
這群人喜歡電腦遊戲嗎？
請注意，這僅是一個教育用的範例。
我們可以構建以下決策樹：
若範例中的人年齡超過15歲
就較不可能喜歡電腦遊戲。
若為15歲以下，且為男性時則很有可能喜歡電子遊戲。
十五歲以下的女性則否。
請注意，決策樹的輸出可以為一項決策，
如：是或否；
但在此情況下，我們將給予其正分和負分。
你很快就會發現這樣的好處。
但這棵決策樹只是接近解答的一種可能，
且沒有甚麼特別的(spectacular one)。

English: 
Dear Fellow Scholars, this is Two Minute Papers
with Károly Zsolnai-Fehér.
A decision tree is a great tool to help making
good decisions from a huge bunch of data.
The classical example is when we have a bunch
of information about people and would like
to find out whether they like computer games
or not. Note that this is a toy example for
educational purposes.
We can build the following tree: if the person's
age in question is over 15, the person is
less likely to like computer games. If the
subject is under 15 and is a male, he is quite
likely to like video games, if she's female,
then less likely.
Note that the output of the tree can be a
decision, like yes or no, but in our case,
we will assign positive and negative scores
instead. You'll see in a minute why that's beneficial.
But this tree wa s just one possible way of
approaching the problem, and admittedly, not
a spectacular one - a different decision tree
could be simply asking whether this person

English: 
uses a computer daily or not.
Individually, these trees are quite shallow
and we call them weak learners. This term
means that individually, they are quite inaccurate,
but slightly better than random guessing.
And now comes the cool part. The concept of
tree boosting means that we take many weak
learners and combine them into a strong learner.
Using the mentioned scoring system instead
of decisions also makes this process easy
and straightforward to implement.
Boosting is similar to what we do with illnesses.
If a doctor says that I have a rare condition,
I will make sure and ask at least a few more
doctors to make a more educated decision about
my health.
The cool thing is that the individual trees
don't have to be great, if they give you decisions
that are just a bit better than random guessing,
using a lot of them will produce strong learning results.
If we go back to the analogy with doctors,
then if the individual doctors know just enough

Chinese: 
另一顆決策樹可以僅僅詢問這些人
是否天天使用電腦。
單獨來看，這些決策樹很淺
我們稱他們為弱學習者。
這個名詞表示，若將這些樹單獨來看，
它們是非常不准確的，但比隨機猜測略勝一籌。
現在有趣的來了。
決策樹提升(boosting)的想法就是
組合多個弱學習器成為一個強的學習器。
使用上面提到的決策評分系統取代決策
也使這個過程變得容易並可直接實行。
Boosting與我們對疾病的處理類似。
如果醫生說我有罕見疾病，
我會確保去至少詢問一些其他醫生
以做出對健康更明智的決策
很酷的是個別的樹並不用有太好的表現
只要比隨機猜測好一點就行，
但若把這些樹集合使用就可以產生很強的學習效果。
回到與醫生的比喻，
若個別醫生僅知道不要殺死病人

English: 
not to kill the patient, a well-chosen committee
will be able to put together an accurate diagnosis
for the patient.
An even cooler, adaptive version of this technique
brings in new doctors to the committee according
to the deficiencies of the existing members.
One other huge advantage of boosted trees
over neural networks is that we actually see
why and how the computer arrives to a decision.
This is a remarkably simple method that leads
to results of very respectable accuracy. A
well-known software library called XGBoost
has been responsible for winning a staggering
amount of machine learning competitions in
Kaggle.
I'd like to take a second to thank you Fellow
Scholars for your amazing support on Patreon
and making Two Minute Papers possible. Creating
these episodes is a lot of hard work and your
support has been invaluable so far, thank
you so much!
We used to have three categories for supporters.
Undergrad students get access to a Patron-only

Chinese: 
那一個精心挑選的醫學委員會
將能準確地診斷病人。
更酷炫的是，升級版的這個Boost技術帶來了新的醫師成員，
而此成員的專長
恰好可以補強目前現有成員的不足之處。
Boost過後的樹在神經網絡有另一個巨大優勢，
就是我們可以實際看到
為什麼以及如何電腦做出這個決定。
這是一種非常簡單的方法
並導向非常可敬的準確性。
在機器學習競賽─Kaggle中，
使用XGBoost（一個著名的軟件庫(lib)）
的贏家占有極大的比例。
我想花一點時間來感謝你們
學者們對Patreon的出色支持
並使兩分鐘論文系列成為可能。
創建這些單元是非常困難地工作
到目前為止，而您的支持是非常寶貴的，謝謝你們！
我們曾經有三個類別的支持者。
本科學生可以訪問贊助人活動feed

Chinese: 
且可提前了解新單元的主題。
博士生沉迷於『兩分鐘論文』將得到
機會提前看到每集最長24小時的內容
說到委員會，全由教授組成的委員會
將會決定決定下幾集的順序。
而現在，我們引入了一個新的類別
：諾貝爾獲獎者。此類別的支持者
可以從字面上成為兩分鐘論文的一部分，
並在影片敘述中被列名
於即將上映的劇集。
並加上所有以上的功能。
感謝收看，感謝您的慷慨支持，
希望下次再會！

English: 
activity feed and get to know well in advance
the topics of the new episodes. PhD students
who are addicted to Two Minute Papers get
a chance to see every episode up to 24 hours
in advance. Talking about committees in this
episode, Full Professors form a Committee
to decide the order of the next few episodes.
And now, we introduce a new category, the
Nobel Laureate. Supporters in this category
can literally become a part of Two Minute
Papers and will be listed in the video description
box in the upcoming episodes. Plus all of
the above.
Thanks for watching, and for your generous
support, and I'll see you next time!
