一般来说,根据经验我们一般选择k=5或10。 2.4 Cross-Validation on Classification Problems 上面我们讲的都是回归问题,所以用MSE来衡量test error。如果是分类问题,那么我们可以用以下式子来衡量Cross-Validation的test error: 其中Erri表示的是第i个模型在第i组测试集上的分类错误的个数。 图片来源:《An Introductio...
一般来说,根据经验我们一般选择k=5或10。 2.4 Cross-Validation on Classification Problems 上面我们讲的都是回归问题,所以用MSE来衡量test error。如果是分类问题,那么我们可以用以下式子来衡量Cross-Validation的test error: 其中 表示的是第i个模型在第i组测试集上的分类错误的个数。 图片来源:《An Introduction ...
一般来说,根据经验我们一般选择k=5或10。 2.4 Cross-Validation on Classification Problems 上面我们讲的都是回归问题,所以用MSE来衡量test error。如果是分类问题,那么我们可以用以下式子来衡量Cross-Validation的test error: 其中Erri表示的是第i个模型在第i组测试集上的分类错误的个数。 图片来源:《An Introductio...
一般来说,根据经验我们一般选择k=5或10。 2.4 Cross-Validation on Classification Problems 上面我们讲的都是回归问题,所以用MSE来衡量test error。如果是分类问题,那么我们可以用以下式子来衡量Cross-Validation的test error: 其中Erri表示的是第i个模型在第i组测试集上的分类错误的个数。 其中Erri表示的是第i个...
Cross-Validation in Fuzzy ARTMAP Neural Networks for Large Sample Classification Problemsfuzzy ARTMAPgeneralization performanceovertrainingcross-validationIn this paper we are examining the issue of overtraining in Fuzzy ARTMAP. Over-training in Fuzzy ARTMAP manifests itself in two different ways: (a) it...
for binary classification problems, each case in the validation set is either predicted correctly or incorrectly. In this situation the misclassification error rate can be used to summarize the fit, although other measures likepositive predictive valuecould also be used. When the value being predicted...
For classification problems,stratified samplingis recommended for creating the folds Each response class should be represented with equal proportions in each of the K folds If dataset has 2 response classes Spam/Ham 20% observation = ham Each cross-validation fold should consist of exactly 20% ham...
for a classification problems: misclassification error. Otherwise, the cross-validation process is exactly the same for all type of problem. Articles Related Model Building - ReSampling Validation Statistics - (Shrinkage|Regularization) of Regression Coefficients Machine Learning - K-Nearest Neighbors (KN...
The described methods are evaluated on classification problems from the European StatLog project. It is hereby shown that the design tools extends the competitiveness of the n-tuple classification method. 展开 关键词: Algorithms Computer programming Neural networks ...
We apply this bound to some classification and regression problems, and compare the results with previously known bounds. One aspec... Z Tong - Conference on Computational Learning Theory 被引量: 49发表: 2001年 Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel...