西格爾零點猜想

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 。 我不得不說這是一本很棒的書。 我如何定義一本很棒的書？ 一本書可以永久改變您的態度。 您一定不能和拾起書之前的那個人一樣。 它會影響您生活的一個或多個方面：個人，財務，社交，浪漫，家庭或職業。 另外，只有在我可以使用從書本中學到的知識的情況下，我才會讀一本書。 我不只是為了學習一些東西而閱讀它。 如果必須在我的書桌上放一本書，它必須立即在我的生活中的一個領域中可用。 因此，是的，這本書不僅對我對數據科學的專業理解產生了重大影響，而且還幫助我發現了自己的興趣。 從這本書中有兩點要學習。 第一個是來自五個效果 ：

The Prediction Effect

預測效果

The Data Effect

數據效果

The Induction Effect

感應效應

The Ensemble Effect

合奏效果

The Persuasion Effect

說服力

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)在不放棄本書思想的前提下，將他在預測分析中的許多經驗隱藏在這些影響中。 預測效果證明，準確度較低的預測比業務中的猜測更好。 數據效應表明數據總是有故事要講的，總有一些值得學習的東西。 歸納效應證明，這是驅動機器學習的藝術。 合奏效應解釋了協同作用的概念如何在預測中有用。 說服效果將營銷技術，業務感知和A / B測試聯系起來。 您可能會認為它是如此簡單，并且您已經知道了，并且可能是對的，除非您在預測分析方面擁有十年的經驗。 而且您仍然可以學習這本書中的新內容。 每種效果均通過實際業務案例進行解釋。 這本書包含了通過應用這些效果而獲得的實際業務成果。 最相反的是他是大學的教授，但是他的寫作風格是務實，非學術性和商業成果導向的。

From Eric Siegel’s Book埃里克·西格爾的書

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.

我從這本書中學到的第二件事是對主題本身的理解。 我參加了一些數據科學課程，并使用Pandas，NumPy和scikit-learn編寫了簡短的程序。 我建立了一些機器學習模型，我以為我知道一些。 我錯了。 這本書教會了我機器學習在現實生活中的有用性。 編寫代碼以構建，測試和評估模型并不能理解機器學習。 本書詳細介紹了什么是機器學習。 無需一行代碼，就可以更詳細地解釋決策樹。 這本身就表明了Eric對機器學習建模的理解。 然后，對重要主題的討論很多，例如相關性并不意味著因果關系 ， 模型如何過度學習以及為何存在訓練測試數據拆分 。 當然，所有這些都與真實的業務案例有關。 看起來像業務風險的Eric使用預測分析將其轉換為機會。 書中沒有一頁他會因為使用預測分析來解決業務問題而失去了重點。

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.

讀完這本書后，我的專業興趣永久地改變了。 現在，我對學習和發現更多有關機器學習如何發現財務欺詐，如何將機器學習程序應用于企業中的營銷或廣告問題以及如何在執法中使用的信息感到非常好奇和非常感興趣。 在閱讀本書之前，我最不感興趣的是所有這些。 我略過了本書的某些部分，但那仍然是一種令人難以置信的經歷。

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

西格爾零點猜想

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