difference between fitcnet and patternnet functions
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Yogini Prabhu
am 19 Mai 2021
Beantwortet: pathakunta
am 26 Jan. 2024
I am not able to get difference between fitcnet and patternnet functions; when to use which one and what change happens in the result, if one replaced by other?
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Conor Daly
am 4 Dez. 2023
fitcnet and patternnet can both be used to solve tabular classification problems.
patternnet is used to define a network architecture which can then be passed to the train function, along with training data, to train a network. fitcnet defines the network architecture and trains the network based on training data in a single line of code.
There are some differences between the two approaches. For example, fitcnet uses the L-BFGS optimizer to train the model. patternnet defaults to the scaled conjugate gradient optimizer -- though others are available. In addition, the ClassificatioNeuralNetwork object returned by fitcnet has properties and methods common to the other fitc* functions for tabular classification -- for example predict, loss and edge.
Finally, note that fitcnet is available in the Classification Learner app, which facilitates easy comparison of multiple machine learning models for tabular classifcation problems.
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Girijashankar Sahoo
am 20 Mai 2021
1. FITNET for regression (MATLAB calls it curve fitting) which is supposed to be a replacement for NEWFF)
2. PATTERNNET for pattern recognition and classification ( which were previously achieved using NEWFF)
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pathakunta
am 26 Jan. 2024
1. FITNET for regression (MATLAB calls it curve fitting) which is supposed to be a replacement for NEWFF) 2. PATTERNNET for pattern recognition and classification ( which were previously achieved using NEWFF)
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