西格爾零點(diǎn)猜想

I finished reading Eric Siegel’s Predictive Analytics. And I have to say it was an awesome read. How do I define an awesome or great book? A book that changes your attitude permanently. You must not be the same person that you were before you picked up the book. It impacts one or more aspects of your life: personal, financial, social, romantic, family, or professional. Also, I read a book only if I can use what I learn from it. I don’t read it just for the sake of learning something. It needs to be practically usable in one of the areas of my life immediately if a book has to be on my desk. So yes, this book made a big impact not only on my professional understanding of data science but it also helped me uncover my interests. There are two primary things to learn from the book. First one is from the five effects:

我讀完了Eric Siegel的Predictive Analytics 。 我不得不說(shuō)這是一本很棒的書(shū)。 我如何定義一本很棒的書(shū)？ 一本書(shū)可以永久改變您的態(tài)度。 您一定不能和拾起書(shū)之前的那個(gè)人一樣。 它會(huì)影響您生活的一個(gè)或多個(gè)方面：個(gè)人，財(cái)務(wù)，社交，浪漫，家庭或職業(yè)。 另外，只有在我可以使用從書(shū)本中學(xué)到的知識(shí)的情況下，我才會(huì)讀一本書(shū)。 我不只是為了學(xué)習(xí)一些東西而閱讀它。 如果必須在我的書(shū)桌上放一本書(shū)，它必須立即在我的生活中的一個(gè)領(lǐng)域中可用。 因此，是的，這本書(shū)不僅對(duì)我對(duì)數(shù)據(jù)科學(xué)的專(zhuān)業(yè)理解產(chǎn)生了重大影響，而且還幫助我發(fā)現(xiàn)了自己的興趣。 從這本書(shū)中有兩點(diǎn)要學(xué)習(xí)。 第一個(gè)是來(lái)自五個(gè)效果 ：

The Prediction Effect

預(yù)測(cè)效果

The Data Effect

數(shù)據(jù)效果

The Induction Effect

感應(yīng)效應(yīng)

The Ensemble Effect

合奏效果

The Persuasion Effect

說(shuō)服力

Without giving away the book’s ideas, Eric has hidden a lot of his experience in predictive analytics into these effects. The prediction effect proves that a lesser accurate prediction is better than a guess in business. The data effect says the data always has a story to tell and there is always something valuable to learn from it. The induction effect proves that it is the art that drives machine learning. The ensemble effect explains how the concept of synergy is useful in prediction. The persuasion effect connects marketing techniques, business-sense, and A/B testing. You might think it is all so simple and you already know it and you might be right unless you have a decade of experience in predictive analytics. And you still can learn something new in the book. Each effect is explained with real-life business cases. The book is filled with practical business results obtained from applying these effects. The most contrasting thing is he was a professor in a university but his writing style is practical, non-academic, and business-results oriented.

埃里克(Eric)在不放棄本書(shū)思想的前提下，將他在預(yù)測(cè)分析中的許多經(jīng)驗(yàn)隱藏在這些影響中。 預(yù)測(cè)效果證明，準(zhǔn)確度較低的預(yù)測(cè)比業(yè)務(wù)中的猜測(cè)更好。 數(shù)據(jù)效應(yīng)表明數(shù)據(jù)總是有故事要講的，總有一些值得學(xué)習(xí)的東西。 歸納效應(yīng)證明，這是驅(qū)動(dòng)機(jī)器學(xué)習(xí)的藝術(shù)。 合奏效應(yīng)解釋了協(xié)同作用的概念如何在預(yù)測(cè)中有用。 說(shuō)服效果將營(yíng)銷(xiāo)技術(shù)，業(yè)務(wù)感知和A / B測(cè)試聯(lián)系起來(lái)。 您可能會(huì)認(rèn)為它是如此簡(jiǎn)單，并且您已經(jīng)知道了，并且可能是對(duì)的，除非您在預(yù)測(cè)分析方面擁有十年的經(jīng)驗(yàn)。 而且您仍然可以學(xué)習(xí)這本書(shū)中的新內(nèi)容。 每種效果均通過(guò)實(shí)際業(yè)務(wù)案例進(jìn)行解釋。 這本書(shū)包含了通過(guò)應(yīng)用這些效果而獲得的實(shí)際業(yè)務(wù)成果。 最相反的是他是大學(xué)的教授，但是他的寫(xiě)作風(fēng)格是務(wù)實(shí)，非學(xué)術(shù)性和商業(yè)成果導(dǎo)向的。

From Eric Siegel’s Book埃里克·西格爾的書(shū)

The second thing I learned from the book is the understanding of the subject itself. I have taken several data science courses and written short programs using Pandas, NumPy, and scikit-learn. I have built a few machine learning models and I thought I knew something. I was wrong. This book taught me the usefulness of machine learning in real-life. Writing code to build, test, and evaluate models is not understanding machine learning. This book gives a detailed explanation of what machine learning is. There is an even more detailed explanation of decision trees without a single line of code. That in itself shows the grip of Eric’s understanding of machine learning modeling. Then there is a good amount of coverage of important topics like correlation does not imply causation, how models over-learn, and why training-test data split exists. Of course, all of it with real-life business cases. What looks like business risk, Eric converts it into an opportunity using predictive analytics. There is not a page in the book where he loses the focus from using predictive analytics to solve business problems.

我從這本書(shū)中學(xué)到的第二件事是對(duì)主題本身的理解。 我參加了一些數(shù)據(jù)科學(xué)課程，并使用Pandas，NumPy和scikit-learn編寫(xiě)了簡(jiǎn)短的程序。 我建立了一些機(jī)器學(xué)習(xí)模型，我以為我知道一些。 我錯(cuò)了。 這本書(shū)教會(huì)了我機(jī)器學(xué)習(xí)在現(xiàn)實(shí)生活中的有用性。 編寫(xiě)代碼以構(gòu)建，測(cè)試和評(píng)估模型并不能理解機(jī)器學(xué)習(xí)。 本書(shū)詳細(xì)介紹了什么是機(jī)器學(xué)習(xí)。 無(wú)需一行代碼，就可以更詳細(xì)地解釋決策樹(shù)。 這本身就表明了Eric對(duì)機(jī)器學(xué)習(xí)建模的理解。 然后，對(duì)重要主題的討論很多，例如相關(guān)性并不意味著因果關(guān)系 ， 模型如何過(guò)度學(xué)習(xí)以及為何存在訓(xùn)練測(cè)試數(shù)據(jù)拆分 。 當(dāng)然，所有這些都與真實(shí)的業(yè)務(wù)案例有關(guān)。 看起來(lái)像業(yè)務(wù)風(fēng)險(xiǎn)的Eric使用預(yù)測(cè)分析將其轉(zhuǎn)換為機(jī)會(huì)。 書(shū)中沒(méi)有一頁(yè)他會(huì)因?yàn)槭褂妙A(yù)測(cè)分析來(lái)解決業(yè)務(wù)問(wèn)題而失去了重點(diǎn)。

My professional interests have permanently changed after reading the book. Now I am curious and very much interested in learning and finding more about how machine learning uncovers financial frauds, how a machine learning program can be applied to marketing or advertising problem in a business, and how it can be used in law enforcement. All of which I was least interested in doing before reading the book. I skipped some parts of the book but still, it was a mind-bending experience.

讀完這本書(shū)后，我的專(zhuān)業(yè)興趣永久地改變了。 現(xiàn)在，我對(duì)學(xué)習(xí)和發(fā)現(xiàn)更多有關(guān)機(jī)器學(xué)習(xí)如何發(fā)現(xiàn)財(cái)務(wù)欺詐，如何將機(jī)器學(xué)習(xí)程序應(yīng)用于企業(yè)中的營(yíng)銷(xiāo)或廣告問(wèn)題以及如何在執(zhí)法中使用的信息感到非常好奇和非常感興趣。 在閱讀本書(shū)之前，我最不感興趣的是所有這些。 我略過(guò)了本書(shū)的某些部分，但那仍然是一種令人難以置信的經(jīng)歷。

翻譯自: https://towardsdatascience.com/what-i-learned-from-eric-siegel-1399e1e6d944

西格爾零點(diǎn)猜想

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