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Lecture Slides
... positive rate and the false positive rate The area under the ROC curve is a measure of the accuracy of the model Rank the test tuples in decreasing order: the one that is most likely to belong to the positive class appears at the top of the list The closer to the diagonal line (i.e., the closer the ...
... positive rate and the false positive rate The area under the ROC curve is a measure of the accuracy of the model Rank the test tuples in decreasing order: the one that is most likely to belong to the positive class appears at the top of the list The closer to the diagonal line (i.e., the closer the ...
Classification with correlated features
... (3) The importance of the variables {wi1 }1≤i≤q and {wi2 }1≤i≤p does not depend on the corresponding group sizes, namely q and p, respectively. We require that property (i) holds because, in absence of a true model, it is wise to give fair chances to all correlated variables for being considered as ...
... (3) The importance of the variables {wi1 }1≤i≤q and {wi2 }1≤i≤p does not depend on the corresponding group sizes, namely q and p, respectively. We require that property (i) holds because, in absence of a true model, it is wise to give fair chances to all correlated variables for being considered as ...
Using Weights to Adjust for Sample Selection When Auxiliary
... functions h·. Assuming that a rich set of moments is available for creating h·, the model can be derived from economic modeling. In this case the estimates of 2 might be of independent interest. The conditioning vector in (5) may also include the dependent variable in the estimation equation. T ...
... functions h·. Assuming that a rich set of moments is available for creating h·, the model can be derived from economic modeling. In this case the estimates of 2 might be of independent interest. The conditioning vector in (5) may also include the dependent variable in the estimation equation. T ...
1. Introduction Generalized linear mixed models
... We can also think of random effects as a way to combine information from different levels within a grouping variable. Suppose that you had estimated photosynthetic rate from multiple individuals from each ...
... We can also think of random effects as a way to combine information from different levels within a grouping variable. Suppose that you had estimated photosynthetic rate from multiple individuals from each ...
Coefficient of determination
In statistics, the coefficient of determination, denoted R2 or r2 and pronounced R squared, is a number that indicates how well data fit a statistical model – sometimes simply a line or a curve. An R2 of 1 indicates that the regression line perfectly fits the data, while an R2 of 0 indicates that the line does not fit the data at all. This latter can be because the data is utterly non-linear, or because it is random.It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model (pp. 187, 287).There are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. In this case, if an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the outcomes and their predicted values. If additional explanators are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination ranges from 0 to 1.Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions that are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept. Additionally, negative values of R2 may occur when fitting non-linear functions to data. In cases where negative values arise, the mean of the data provides a better fit to the outcomes than do the fitted function values, according to this particular criterion.