The Practical Importance of Feature Selection（變量篩選重要性）

python信用評分卡（附代碼，博主錄製）

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原文連接

https://www.kdnuggets.com/2017/06/practical-importance-feature-selection.html

 

Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.

特徵選擇在各個方面都頗有用：它是反對過分擬合的最佳武器; 它能夠減小總體培訓時間; 它是對過分擬合，增長廣泛性的有力防護。

 

By Matthew Mayo, KDnuggets.

If you wanted to classify animals, for example, based on a plethora of relevant collected data, you would quickly find that all sorts of potential data attributes, or features, were relatively unhelpful for classification. For example, given that most living creatures have precisely 1 heart, this particular feature would not be beneficial, from a learning perspective. On the other hand, an attribute denoting whether or not a given animal is hoofed would likely be a powerful predictor.

若是您想對動物進行分類，例如，基於過多的相關收集數據，您會很快發現各類潛在的數據屬性或特徵對於分類而言相對無益。例如，鑑於大多數生物只有1顆心臟，從學習的角度來看，這一特殊功能並非有益的。另外一方面，表示給定動物是否有蹄的屬性多是強有力的預測因子。

 

Further, using all of these irrelevant attributes, mixed in with the powerful predictors, may actually have a negative effect on the resulting model. This is to say nothing of the increased training times that may come along with the inclusion of useless attributes, or the overfitting which may occur on the training data.

此外，使用全部這些無關屬性，與強大的預測變量混合，實際上可能對結果模型產生負面影響。這也就是說，可能伴隨着包含無用屬性或訓練數據可能出現的過分擬合而增長的訓練時間。

 

Feature selection is the process of narrowing down a subset of features, or attributes, to be used in the predictive modeling process. Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing model generalizability.

特徵選擇是縮小要在預測建模過程當中使用的特徵或屬性子集的過程。特徵選擇在各個方面都頗有用：它是反對維度詛咒的最佳武器; 它能夠減小總體培訓時間; 它是對過分擬合的強大防護，增長了模型的廣泛性。

 

Something I read recently -- written so eloquently and concisely by data scientist Rubens Zimbres -- alludes to the importance of feature selection from a practical standpoint:

After some experiences, using stacked neural nets, parallel neural nets, asymmetric configs, simple neural nets, multiple layers, dropouts, activation functions etc there is one conclusion: There's NOTHING like a good Feature Selection.

Having had some previous professional contacts with Rubens Zimbres in the past, I reached out to him for some elaboration. He provided the following:

Feature selection should be one of the main concerns for a Data Scientist. Accuracy and generalization power can be leveraged by a correct feature selection, based in correlation, skewness, t-test, ANOVA, entropy and information gain.

Many times a correct feature selection allows you to develop simpler and faster Machine Learning models. Consider the picture below (Support Vector Machine classification of the IRIS dataset): on the left side a wrong variable selection is presented. The linear kernel cannot handle the classification task properly, neither the radial basis function kernel. On the right side, petal width and petal length were selected as features and even the linear kernel is quite accurate. A correct variable selection, a good algorithm choice and hyperparameter tuning are the keys to success. Picture below made with Python.

特徵選擇應該是數據科學家的主要關注點之一。基於相關性，偏度，t檢驗，ANOVA，熵和信息增益，經過正確的特徵選擇能夠利用準確性和泛化能力。

不少時候，正確的功能選擇可讓您開發更簡單，更快速的機器學習模型。考慮下面的圖片（IRIS數據集的支持向量機分類）：在左側顯示錯誤的變量選擇。線性內核沒法正確處理分類任務，也不能處理徑向基函數內核。在右側，選擇花瓣寬度和花瓣長度做爲特徵，甚至線性內核也很是準確。正確的變量選擇，良好的算法選擇和超參數調整是成功的關鍵。下面用Python製做的圖片。

In a time when ample processing power can tempt us to think that feature selection may not be as relevant as it once was, it's important to remember that this only accounts for one of the numerous benefits of informed feature selection -- decreased training times. As Zimbres notes above, with a simple concrete example, feature selection can quite literally mean the difference between valid, generalizable models and a big waste of time.

在充足的處理能力能夠誘使咱們認爲特徵選擇可能不像之前那樣具備相關性的時代，重要的是要記住，這僅僅是知情特徵選擇的衆多好處之一 - 減小了訓練時間。 正如Zimbres上面所說，經過一個簡單的具體例子，特徵選擇能夠徹底意味着有效的，可推廣的模型之間的差別和浪費大量時間。

 

 

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