difference between fitcnet and patternnet functions
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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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Accepted Answer
Conor Daly
on 4 Dec 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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More Answers (2)
Girijashankar Sahoo
on 20 May 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)
pathakunta
on 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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