改善機器學習可解釋性的技術和方法
盡管透明性和道德問題對於現場的數據科學家來說可能是抽象的，但實際上，可以做一些實際的事情來提高算法的可解釋性
算法概括
首先是提高概括性。這聽起來很簡單，但並非那么簡單。當您認為大多數機器學習工程都以非常特定的方式應用算法來發現所需的特定結果時，模型本身可能會感覺像是次要元素-僅僅是達到目的的一種手段。但是，通過改變這種態度來考慮算法的整體運行狀況以及運行該算法的數據，您可以開始為改善可解釋性奠定堅實的基礎。
注意feature importance
這應該很明顯，但是很容易錯過。仔細研究算法各種feature是一種切實可行的方法，可以實際解決從業務調整到道德等一系列問題。關於如何設置每個feature的辯論和討論可能會花費一些時間，但是默契地意識到以某種方式設置了不同的feature仍然是邁向可解釋性重要一步。
LIME：本地可解釋模型不可知的解釋
盡管上述技術提供了數據科學家可以采取的實際步驟，但LIME是研究人員開發的一種實際方法，旨在使算法內部發生的事情更加透明。研究人員解釋說，LIME可以“通過在預測周圍局部學習一個可解釋的模型，以一種可解釋和忠實的方式解釋任何分類器的預測。”
在實踐中，這意味着LIME模型通過對其進行測試來觀察模型中某些方面發生變化時會發生什么，從而發展出該模型的近似值。本質上，它是關於通過實驗過程嘗試從相同的輸入重新創建輸出。
DeepLIFT（深度學習重要功能）
在深度學習特別棘手的領域，DeepLIFT是有用的模型。它通過反向傳播的形式起作用：它獲取輸出，然后嘗試通過“讀取”已形成原始輸出的各種神經元來將其分開。
本質上，這是一種追溯算法內部特征選擇的方法（顧名思義）。
逐層相關性傳播
逐層相關性傳播與DeepLIFT類似，因為它從輸出向后工作，識別出神經網絡中最相關的神經元，直到您返回到輸入（例如，圖像）為止。如果您想了解更多有關該概念背后的數學知識，Dan Shiebler的這篇文章是一個很好的起點。
Techniques and methods for improving machine learning interpretability
While questions of transparency and ethics may feel abstract for the data scientist on the ground, there are, in fact, a number of practical things that can be done to improve an algorithm’s interpretability and explainability.
Algorithmic generalization
The first is to improve generalization. This sounds simple, but it isn’t that easy. When you think most machine learning engineering is applying algorithms in a very specific way to uncover a certain desired outcome, the model itself can feel like a secondary element - it’s simply a means to an end. However, by shifting this attitude to consider the overall health of the algorithm, and the data on which it is running, you can begin to set a solid foundation for improved interpretability.
Pay attention to feature importance
This should be obvious, but it’s easily missed. Looking closely at the way the various features of your algorithm have been set is a practical way to actually engage with a diverse range of questions, from business alignment to ethics. Debate and discussion over how each feature should be set might be a little time-consuming, but having that tacit awareness that different features have been set in a certain way is nevertheless an important step in moving towards interpretability and explainability.
LIME: Local Interpretable Model-Agnostic Explanations
While the techniques above offer practical steps that data scientists can take, LIME is an actual method developed by researchers to gain greater transparency on what’s happening inside an algorithm. The researchers explain that LIME can explain “the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction.”
What this means in practice is that the LIME model develops an approximation of the model by testing it out to see what happens when certain aspects within the model are changed. Essentially it’s about trying to recreate the output from the same input through a process of experimentation.
DeepLIFT (Deep Learning Important Features)
DeepLIFT is a useful model in the particularly tricky area of deep learning. It works through a form of backpropagation: it takes the output, then attempts to pull it apart by ‘reading’ the various neurons that have gone into developing that original output.
Essentially, it’s a way of digging back into the feature selection inside of the algorithm (as the name indicates).
Layer-wise relevance propagation
Layer-wise relevance propagation is similar to DeepLIFT, in that it works backwards from the output, identifying the most relevant neurons within the neural network until you return to the input (say, for example, an image). If you want to learn more about the mathematics behind the concept, this post by Dan Shiebler is a great place to begin.