Sentence examples for by reducing the features from inspiring English sources

By reducing the features in the model using machine learning to identify relevant features, and randomizing the training data using target shuffling (Nisbet et al., 2009), we decreased the risk of overfitting and helped ensure the patterns detected by each modeling approach were unlikely to be due to chance.

The toroidal hexapole (T3) improves the stability diagram by reducing the jagged features at the stability apex and reducing the βr + 2βz = 2 resonance line when the A3 coefficient of the toroidal hexapole is in the range of 2.75.

Bolon-Canedo et al. [18] combined discretizers, filters and classifiers to improve the classification performance by significantly reducing the feature set.

Classifier results are provided in Table 2. Reducing the number of features increases accuracy in some cases for example, by reducing the number of features from 40,000 to 500, accuracy increases by seven percent.

Additionally, by reducing the number of features used to train a model, the computational complexity of the task is reduced.

This problem can be addressed by reducing the number of features (input variables) or increasing the number of samples.

Additional speedup was obtained by reducing the dimensionality of the feature space via principal component analysis.

In contrast to the influence "skipping" data had in the D14_ALP models, the performance of D21_Ca models was enhanced by reducing the frequency of morphological features to 48-h intervals.

While the focus of the voice classification model is centered at the preprocessing steps which leverage the features from both time and frequency domains followed by feature selection for reducing the feature space dimension, classification algorithms can become flexible plug-and-play in our model design.

GP's best model resulted in 0.93% better classification accuracy by reducing the dimension n of the feature space from 256 to 15.

This suggests that the use of selected subset of features enhances prediction performance by reducing the noise created by the redundant and irrelevant features.