The performance of a predictive method, according to a given criterion, can also be represented graphically by means of a lift chart (Witten and Frank, 2000), such as that shown in Fig.
Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method
Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



Sample text:```For example, Kawato et al. (1990) suggested that movement trajectories can be projected using critical via-points through which the trajectory has to pass at a certain time, rather than in terms of the moment-by-moment dynamics of the implemented movement trajectory. Optimal trajectories can then be learnt by applying local optimising principles for getting from one via-point to the next. In this way, impoverished predictions can be used to monitor rich implementations. In the same way, language users might predict particularly crucial aspects of an utterance, but the aspects that they predict will depend on the circumstances. More generally, Meyer & Hagoort question the value of predicting one’s own utterance. They argue that prediction is useful when it is likely to differ from the actual event. This is the case when predicting another person’s behaviour or when the result of one’s action is uncertain (e.g., moving in a strong wind). But they argue that people are confident about their own speech. Meyer & Hagoort admit that they will tend to be less confident in dialogue, and we agree. But more important, we argue that the behaviour of the production implementer is not fully determined by the production command, because the complex processes involved in production are subject to internal (“neural”) noise or priming (i.e., influences that may not be a result of the production command). Assuming that these sources of noise do not necessarily affect forward modelling as well, predicted speech may differ from actual speech. In addition, prediction is useful even if the behaviour is fully predictable, because it allows the actor to plan future behaviour on the basis of the prediction. In fact, we made such a proposal in relation to the order of heavy and light phrases (see sect. 3.1, target article). Hartsuiker claims that our account incorrectly predicts an early competitor effect in Huettig and Hartsuiker (2010). His claim is based on the assumption that Response/Pickering & Garrod: An integrated theory of language production and comprehension```
Sample intent: method



Sample text:```Estimation of overall mortality ratio (relative risk) was carried out using the random e ects model according to DerSimonian and Laird [9].```
Sample intent: background



Sample text:```rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49].```
Sample intent: background
Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



Sample text:```In-between methods can be subdivided into linear approximations [12,24] and quadratic interpolation methods [22,23,25–27] based on the degree of the polynomial approximation employed.```
Sample intent: method



Sample text:```The items are internally consistent and reproducible and the questionnaire may therefore be considered to have at least as favourable levels of reliability as clinical scores used to assess outcomes (Koran 1975).```
Sample intent: result



Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result



Sample text:```This section contains a generalisation of the basic results on multifractal products of stochastic processes developed in Kahane (1985), Kahane (1987) and Mannersalo et al (2002).```
Sample intent: method



Sample text:```rural, we calculated the relative prevalence of a missing value by other demographic and fitness variables using logistic regression [49].```
Sample intent: background



Sample text:```The area under the curve represents the probability that the logistic regression model will rank a randomly chosen positive instance (i.e., an error) higher than a randomly chosen negative instance (i.e., nonerror; Fawcett, 2006; Macmillan & Creelman, 2005).```
Sample intent: method



Sample text:```…between output and input, CNF achieves almost 10% relative improvement over CRF. CNF also outperforms other methods including SVMpro (Hua and Sun, 2001), SVMpsi (Kim and Park, 2003), YASSPP (Karypis, 2006), PSIPRED (Jones, 1999), SPINE (Dor and Zhou, 2007) and TreeCRFpsi (Dietterich et al., 2004).```
Sample intent: background



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result



Sample text:```While this method is known to be generally reliable, there are some questions about the representativeness of the data used [25].```
Sample intent: background