If a model is overfitted that means decent gap between training curve and testing/validation curve but somehow achieves good precision and recall score,does that still indicate that the model is decent?
Overfitting refers to a model that models the training data too well. when , it goes into new data , the prediction will be poor . If you see low error on training set and high error on test & validation set then you have likely over-fitted the model. Or, if both are low, test your model in the wild, on unseen data .
https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
https://statisticsbyjim.com/regression/overfitting-regression-models/
https://elitedatascience.com/overfitting-in-machine-learning
https://www.researchgate.net/post/How_can_we_solve_an_overfitting_problem
https://www.analyticsvidhya.com/blog/2015/02/avoid-over-fitting-regularization/
What is clear is that the applicability of a trained network is evaluated by its prediction capability (more importantly than the training phase). Hence, the results of an over-fitted network can not be presented, because the training samples are extraordinarily adopted and the testing samples represent a poor estimation capability.
Moreover, you may be asked about the solution you have considered for preventing over-fitting. In some cases that the network is susceptible to over-fitting, a third group of data, called "validation data" can be defined. it can be easily defined in ANN and ANFIS.
If the model is Overfitted means you have a very less ratio of (incorrect results)/(correct results) but only for the training set.
But if you predict your model for a test set it might give some seriously deviated results which are far away from the original value.
You can identity overfitting by changing the Value of " random_state " parameter in you object of a particular class.
But if your model is overfitted, It means that it is not accurate and will not produce good results for predictions/classification.
So you must devise a new approach.
Dear, Hafez Ahmad Hardik Gupts Mohammad Mehrabi
how do you define this model : https://www.kaggle.com/kmader/tensorflow-data-keras-for-tuberculosis
validation accuracy around 87% and accuracy around 97% which indicates decent gap between training and validation curve(indicating overfitting) but if you see classification report,it gets high precision,recall and f1 score,do you think this model will generalize well?
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