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Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes.The method was invented by John Platt in the context of support vector machines, [1] replacing an earlier method by Vapnik, but can be applied to other classification models. [2]
The use of different model parameters and different corpus sizes can greatly affect the quality of a word2vec model. Accuracy can be improved in a number of ways, including the choice of model architecture (CBOW or Skip-Gram), increasing the training data set, increasing the number of vector dimensions, and increasing the window size of words ...
Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. [10] It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction.
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
This invocation of an outside execution model is the defining characteristic of a programming model, in contrast to a programming language. In parallel computing, the execution model often must expose features of the hardware in order to achieve high performance. The large amount of variation in parallel hardware causes a concurrent need for a ...
The first two population distribution parameters and are usually characterized as location and scale parameters, while the remaining parameter(s), if any, are characterized as shape parameters, e.g. skewness and kurtosis parameters, although the model may be applied more generally to the parameters of any population distribution with up to four ...
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).