The Practical Importance of Feature Selection（变量筛选重要性）

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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.

特征选择在各个方面都很有用：它是反对过度拟合的最佳武器; 它可以减少整体培训时间; 它是对过度拟合，增加普遍性的有力防御。

 

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.

如果您想对动物进行分类，例如，基于过多的相关收集数据，您会很快发现各种潜在的数据属性或特征对于分类而言相对无益。例如，鉴于大多数生物只有1颗心脏，从学习的角度来看，这一特殊功能并不是有益的。另一方面，表示给定动物是否有蹄的属性可能是强有力的预测因子。

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，使用所有这些无关属性，与强大的预测变量混合，实际上可能对结果模型产生负面影响。这也就是说，可能伴随着包含无用属性或训练数据可能出现的过度拟合而增加的训练时间。

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.

特征选择是缩小要在预测建模过程中使用的特征或属性子集的过程。特征选择在各个方面都很有用：它是反对维度诅咒的最佳武器; 它可以减少整体培训时间; 它是对过度拟合的强大防御，增加了模型的普遍性。

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.

特征选择应该是数据科学家的主要关注点之一。基于相关性，偏度，t检验，ANOVA，熵和信息增益，通过正确的特征选择可以利用准确性和泛化能力。

很多时候，正确的功能选择可以让您开发更简单，更快速的机器学习模型。考虑下面的图片（IRIS数据集的支持向量机分类）：在左侧显示错误的变量选择。线性内核无法正确处理分类任务，也不能处理径向基函数内核。在右侧，选择花瓣宽度和花瓣长度作为特征，甚至线性内核也非常准确。正确的变量选择，良好的算法选择和超参数调整是成功的关键。下面用Python制作的图片。

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.

在充足的处理能力可以诱使我们认为特征选择可能不像以前那样具有相关性的时代，重要的是要记住，这仅仅是知情特征选择的众多好处之一 - 减少了训练时间。 正如Zimbres上面所说，通过一个简单的具体例子，特征选择可以完全意味着有效的，可推广的模型之间的差异和浪费大量时间。

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