We propose a simple method for evaluating the model that has been chosen by an adaptive regression procedure, our main focus being the lasso. This procedure deletes each chosen predictor and refits the lasso to get a set of models that are “close” to the chosen “base model,” and compares the error rates of the base model with that of nearby models. If the deletion of a predictor leads to significant deterioration in the model's predictive power, the predictor is called indispensable; otherwise, the nearby model is called acceptable and can serve as a good alternative to the base model. This provides both an assessment of the predictive contribution of each variable and a set of alternative models that may be used in place of the chosen model. We call this procedure “Next‐Door analysis” since it examines models “next” to the base model. It can be applied to supervised learning problems with ℓ₁ penalization and stepwise procedures. We have implemented it in the R language as a library to accompany the well‐known glmnet library. The Canadian Journal of Statistics 48: 447–470; 2020 © 2020 Statistical Society of Canada

中文翻译：

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通过“Next-Door”分析进行模型拟合探索

我们提出了一种简单的方法来评估由自适应回归程序选择的模型，我们的主要重点是套索。此过程删除每个选择的预测器并重新调整套索以获得一组与所选“基本模型”“接近”的模型，并将基本模型的错误率与附近模型的错误率进行比较。如果删除一个预测器导致模型的预测能力显着下降，则该预测器被称为不可缺少的；否则，附近的模型被称为可接受的并且可以作为基本模型的良好替代品。这既提供了对每个变量的预测贡献的评估，也提供了一组可替代所选模型的替代模型。我们将此过程称为“Next-Door 分析”，因为它检查的是基础模型“旁边”的模型。ℓ ₁惩罚和逐步程序。我们已经用 R 语言将它作为一个库来实现，以配合著名的glmnet库。加拿大统计杂志48：447–470；2020 © 2020 加拿大统计学会