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  2. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    To calculate the recall for a given class, we divide the number of true positives by the prevalence of this class (number of times that the class occurs in the data sample). The class-wise precision and recall values can then be combined into an overall multi-class evaluation score, e.g., using the macro F1 metric. [21]

  3. Probabilistic neural network - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_neural_network

    Each neuron in the input layer represents a predictor variable. In categorical variables, N-1 neurons are used when there are N number of categories. It standardizes the range of the values by subtracting the median and dividing by the interquartile range. Then the input neurons feed the values to each of the neurons in the hidden layer.

  4. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  5. Positive and negative predictive values - Wikipedia

    en.wikipedia.org/wiki/Positive_and_negative...

    The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.

  6. Statistical learning theory - Wikipedia

    en.wikipedia.org/wiki/Statistical_learning_theory

    The learning problem consists of inferring the function that maps between the input and the output, such that the learned function can be used to predict the output from future input. Depending on the type of output, supervised learning problems are either problems of regression or problems of classification .

  7. Accuracy and precision - Wikipedia

    en.wikipedia.org/wiki/Accuracy_and_precision

    Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]

  8. Israel's Netanyahu condemns settler violence on IDF in West Bank

    www.aol.com/news/israels-netanyahu-condemns...

    Prime Minister Benjamin Netanyahu condemned on Sunday Jewish settlers who attacked senior Israeli military officers including Major General Avi Bluth, the head of the army's Central Command in the ...

  9. Capsule neural network - Wikipedia

    en.wikipedia.org/wiki/Capsule_neural_network

    Formally, a capsule is a set of neurons that collectively produce an activity vector with one element for each neuron to hold that neuron's instantiation value (e.g., hue). [1] Graphics programs use instantiation value to draw an object. Capsnets attempt to derive these from their input.