Sentence examples for estimation of the generalization from inspiring English sources

The Random Forest method has many advantages compared with other machine learning methods including: high accuracy, speed, resistance to overfitting, the ability to use heterogeneous training data without rescaling, estimation of the generalization error during training, and the ability to estimate the contribution of each variable to the overall prediction accuracy.

Fair estimation of the generalization performance.

A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power.

A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power.

Each network is trained using tenfold CV technique for an improved estimation of the generalization error of the network.

Also, there are some improvements seen in the reduction of the standard deviations by increasing fold partitions to 5, 10 and 20-fold, but there appears to be marginal benefit, from an estimation of the generalization error perspective, in progressing past 10-fold.

The leave-one-out method is known to be an almost unbiased estimation of the true generalization error; that is, the performance reported with the leave-one-out method is the most similar to the performance this system would show on an unseen test dataset of infinite length once it is trained on all available data (Vapnik, 1982).

The leave-one-out method is known to be an almost unbiased estimation of the true generalization error (Vapnik, 1982); that is the performance reported with the leave-one-out method is the most similar to the performance this system would show on all unseen data of infinite size once it is trained on all available data.

We also show that the CBPS can be extended to other important settings, including the estimation of the generalized propensity score for non-binary treatments and the generalization of experimental estimates to a target population.

Our algorithm facilitates the computation of accuracy of a DTM by comparison with another and it is applicable in such fields as hydrology (precision estimation of the hydrological features), cartographic generalization, and civil engineering.

In particular, Da Prato and Zabczyk [6] studied the problem (1.1) by the semigroup method, based on the classical fixed point theorem, and used the factorization method to get an estimation of the stochastic convolution, which is a generalization of maximal inequality of martingales to stochastic convolution, and plays an important role in the following sections.