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This article needs additional citations for verification. 0, released on March 16, 1999, is the first operating system released into the retail market by Apple Computer based on NeXT technology. 0 contains a mix of features from the classic Mac OS, NeXTSTEP and Mac OS X. Like classic Mac OS, it has a single menu bar across the top of the screen, but file management is performed in Workspace Manager from NeXTSTEP instead of the classic Mac OS Finder. Carbon”, essentially a subset of “classic” Mac OS API calls, was also absent.
0 were written for the “Yellow Box” API, which went on to become known as “Cocoa”. 0 includes the “Blue Box”, which essentially ran a copy of Mac OS 8. Archived from the original on 2008-05-14. Rhapsody Media – Identifying what media you have”. Software Derived from Mac OS X 10.
Please cite us if you use the software. Model evaluation: quantifying the quality of predictions 3. This is not discussed on this page, but in each estimator’s documentation. Model selection and evaluation using tools, such as model_selection.
All scorer objects follow the convention that higher return values are better than lower return values. Thus metrics which measure the distance between the model and the data, like metrics. The scorer objects for those functions are stored in the dictionary sklearn. Metrics available for various machine learning tasks are detailed in sections below. In such cases, you need to generate an appropriate scoring object. That function converts metrics into callables that can be used for model evaluation. If a loss, the output of the python function is negated by the scorer object, conforming to the cross validation convention that scorers return higher values for better models.
Again, by convention higher numbers are better, so if your scorer returns loss, that value should be negated. Note that the dict values can either be scorer functions or one of the predefined metric strings. Currently only those scorer functions that return a single score can be passed inside the dict. Some metrics might require probability estimates of the positive class, confidence values, or binary decisions values. Cohen’s kappa: a statistic that measures inter-annotator agreement.
Log loss, aka logistic loss or cross-entropy loss. In the following sub-sections, we will describe each of those functions, preceded by some notes on common API and metric definition. In extending a binary metric to multiclass or multilabel problems, the data is treated as a collection of binary problems, one for each class. There are then a number of ways to average binary metric calculations across the set of classes, each of which may be useful in some scenario. In problems where infrequent classes are nonetheless important, macro-averaging may be a means of highlighting their performance. On the other hand, the assumption that all classes are equally important is often untrue, such that macro-averaging will over-emphasize the typically low performance on an infrequent class. Rather than summing the metric per class, this sums the dividends and divisors that make up the per-class metrics to calculate an overall quotient.
Micro-averaging may be preferred in multilabel settings, including multiclass classification where a majority class is to be ignored. In multilabel classification, the function returns the subset accuracy. If the entire set of predicted labels for a sample strictly match with the true set of labels, then the subset accuracy is 1. It is the macro-average of recall scores per class or, equivalently, raw accuracy where each sample is weighted according to the inverse prevalence of its true class. Thus for balanced datasets, the score is equal to accuracy. ROC curve with binary predictions rather than scores.
1, inclusive, with performance at random scoring 0. Class balanced accuracy as described in : the minimum between the precision and the recall for each class is computed. Those values are then averaged over the total number of classes to get the balanced accuracy. Balanced Accuracy as described in : the average of sensitivity and specificity is computed for each class and then averaged over total number of classes. Mosley, A balanced approach to the multi-class imbalance problem, IJCV 2010.