judgment selection - ορισμός. Τι είναι το judgment selection
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  • πώς χρησιμοποιείται η λέξη
  • συχνότητα χρήσης
  • χρησιμοποιείται πιο συχνά στον προφορικό ή γραπτό λόγο
  • επιλογές μετάφρασης λέξεων
  • παραδείγματα χρήσης (πολλές φράσεις με μετάφραση)
  • ετυμολογία

Τι (ποιος) είναι judgment selection - ορισμός

PROCEDURE IN MACHINE LEARNING AND STATISTICS
Input selection; Feature selection problem; Variable selection; Feature subset selection
  • Embedded method for Feature selection
  • Wrapper Method for Feature selection
  • Filter Method for feature selection

entry of judgment         
THE FORMAL DECISION MADE BY A COURT FOLLOWING A LAWSUIT
Court decision; Enter a judgment; Entry of judgment; Reserved decision; Legal judgment; Money judgment; Monetary judgment; Reserved judgment; Judgement (law); Legal judgement; Reserved judgement
n. the placement of a judgment on the official roll of judgments.
Feature selection         
In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons:
Judgment (law)         
THE FORMAL DECISION MADE BY A COURT FOLLOWING A LAWSUIT
Court decision; Enter a judgment; Entry of judgment; Reserved decision; Legal judgment; Money judgment; Monetary judgment; Reserved judgment; Judgement (law); Legal judgement; Reserved judgement
In law, a judgment, also spelled judgement, is a decision of a court regarding the rights and liabilities of parties in a legal action or proceeding.Black’s Law Dictionary 970 (10th ed.

Βικιπαίδεια

Feature selection

In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons:

  • simplification of models to make them easier to interpret by researchers/users,
  • shorter training times,
  • to avoid the curse of dimensionality,
  • improve data's compatibility with a learning model class,
  • encode inherent symmetries present in the input space.

The central premise when using a feature selection technique is that the data contains some features that are either redundant or irrelevant, and can thus be removed without incurring much loss of information. Redundant and irrelevant are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated.

Feature selection techniques should be distinguished from feature extraction. Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features. Feature selection techniques are often used in domains where there are many features and comparatively few samples (or data points). Archetypal cases for the application of feature selection include the analysis of written texts and DNA microarray data, where there are many thousands of features, and a few tens to hundreds of samples.