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  2. statistical vs overfit

different between statistical vs overfit

statistical

English

Etymology

statistic +? -al

Pronunciation

  • (UK, US) IPA(key): /st??t?st?k?l/

Adjective

statistical (comparative more statistical, superlative most statistical)

  1. Of or pertaining to statistics.

Derived terms

Translations

statistical From the web:

  • what statistical test to use
  • what statistical test to use chart
  • what statistical question
  • what statistical mean
  • what statistical test to use quiz
  • what statistical test to compare two groups
  • what statistical measures the variability of a distribution
  • what statistical test to use for nominal data

overfit

English

Etymology

over- +? fit

Verb

overfit (third-person singular simple present overfits, present participle overfitting, simple past and past participle overfitted)

  1. (statistics) To use a statistical model that has too many parameters relative to the size of the sample leading to a good fit with the sample data but a poor fit with new data.
    Antonym: underfit
    • 2004, Mark T.D. Cronin, Predicting Chemical Toxicity and Fate, CRC Press (?ISBN), page 169:
      In all modeling techniques, and neural networks in particular, care must be taken not to overtrain or overfit the model. If possible, models should be interpreted in terms of their mechanistic meaning.
    • 2007, Kenneth P. Burnham, David R. Anderson, Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, Springer Science & Business Media (?ISBN), page 250:
      If your strategy is to always fit and use the global model, you will probably overfit the model (i.e., include unnecessary variables).

Translations

See also

  • overfit stream

overfit From the web:

  • overfitting what to do
  • overfit what means
  • overfitting what is bias
  • what is overfitting in machine learning
  • what is overfitting a model
  • what is overfitting and underfitting in machine learning
  • what is overfitting in neural networks
  • what causes overfitting
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