• * train the model on training set * evaluate the model on the training set
    4 KB (524 words) - 22:55, 8 June 2014
  • ! Example Model ...additionally we have a cross-validation set to test the performance of our model depending on the parameter
    7 KB (1,145 words) - 23:03, 8 June 2014
  • Suppose you created a model, but when you tested it, you found that it makes large errors ...arning algorithms and what is not, and gain guidance as how to improve the performance.
    4 KB (505 words) - 22:34, 18 December 2017
  • == Evaluation of Binary Classifiers == Evaluation is important:
    8 KB (1,073 words) - 22:51, 28 April 2015
  • * so it's a way of showing the performance of Binary Classifiers ** see [[Evaluation of Binary Classifiers]]
    15 KB (2,223 words) - 14:16, 18 January 2015
  • Gain Charts are used for [[Evaluation of Binary Classifiers]] * we build a model that scores receivers - assigns probability that he will reply
    7 KB (1,035 words) - 22:50, 8 June 2014
  • [[Category:Model Performance Evaluation]]
    2 KB (227 words) - 22:52, 8 June 2014
  • == True Error of Model == What do we do when we want to know how accurately the model ''will'' perform in practice
    2 KB (309 words) - 23:01, 8 June 2014
  • * best accuracy or $F_1$-score (see [[Evaluation of Binary Classifiers]]) * best [[True Error of Model]]
    4 KB (520 words) - 00:03, 6 December 2014
  • * the closer the curve to the $(1, 1)$ point - the better the IR system performance how to measure overall performance of an IR system?
    12 KB (1,819 words) - 16:39, 22 December 2015
  • A statistical language model (Language Model for short) is a probability distribution over sequences of words (i.e. over ...nces are valid, sentence 1 is more likely: for example, because the LM can model some general conversations rather than conversations on a math conference
    5 KB (828 words) - 13:45, 27 June 2015