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Returns the cumulative density/mass function evaluated at value . >>> m = Categorical ( torch . tensor ([ 0.25 , 0.25 , 0.25 , 0.25 ])) >>> m . sample () # equal probability of 0, 1, 2, 3 tensor(3) arg_constraints : Dict [ str , Constraint ] = ¶ cdf ( value ) [source] ¶ entropy ( ) [source] ¶ expand ( batch_shape , _instance = None ) [source] ¶ has_rsample = True ¶ icdf ( prob ) [source] ¶ log_prob ( value ) [source] ¶ property mean ¶ property mode ¶ property scale ¶ support = GreaterThanEq(lower_bound=0.0) ¶ property variance ¶ Creates a multivariate normal (also called Gaussian) distribution parameterized by a mean vector and a covariance matrix. Note that the .event_shape of a TransformedDistribution is the maximum shape of its base distribution and its transforms, since transforms can introduce correlations among events. Transform functor that applies a sequence of transforms tseq component-wise to each submatrix at dim , of length lengths[dim] , in a way compatible with torch.cat() . alias of _DependentProperty. F-measure has an intuitive meaning. Como editar o arquivo .htaccess através do pin up 400 Gerenciador de Arquivos no cPanel. Accuracy is used when the True Positives and True negatives are more important while F1-score is used when the False Negatives and False Positives are crucial Accuracy can be used when the class distribution is similar while F1-score is a better metric when there are imbalanced classes as in the above case. In most real-life classification problems, imbalanced class distribution exists and thus F1-score is a better metric to evaluate our model on. defined hyperparameter values: You can log different kinds of metadata to Neptune, including metrics, charts, parameters, images, and more. Entra no sportingbet.FC Barcelona vs. Chelsea FC.
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