Need help understanding regression plots

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NASRIN AKTER il 14 Ott 2021
Modificato: Aneela il 20 Feb 2024 alle 9:02
I have two datasets which I used interchangeably to train and test a function fitting neural network (fitnet). For example, dataset 1 for training, dataset 2 for testing and vice versa. I used 'plotregression' to see the results but I am not sure which parameters are the actual performance metrics here. If the performance is bad, how can I improve it? Can someone please help?

Risposte (1)

Aneela il 20 Feb 2024 alle 8:54
Modificato: Aneela il 20 Feb 2024 alle 9:02
Hi Nasrin Akter,
I can infer that you have used “plotregression” to visualize how well the model is performing.
It plots the actual targets against the predicted outputs.
  • The x-axis represents target values (actual values),’T’ in your case.
  • The y-axis represents the output values (predicted values), ‘Y’ in your case.
  • The diagonal, dotted line (Y=T) represents the line of perfect fit.
  • The blue line “Fit”, represents the line of regression with respect to the data points.
The data points, “Data” are plotted with respect to the predicted and actual values.
The variable “R” refers to the correlation coefficient between the targets and outputs of the neural network.
R value of 1 indicates perfect correlation, while an R value of 0 indicates no correlation.
  • In your case, an R value of 0.72811 for first plot suggests a moderate to good correlation, but an R value of 0.30238 for second plot indicates a poor correlation, suggesting that the model is not generalizing well to unseen data.
Typical methods that can be used for improving the performance of the Neural Networks are as follows:
  • Normalize or standardize input data.
  • For tasks like image and speech recognition, you can use data augmentation techniques to artificially increase the diversity of your training set by applying random transformations.
  • Apply regularization techniques such as L1, L2, or dropout to prevent overfitting.
  • Experiment with different learning rates, batch sizes, and other hyperparameters.
  • Use techniques like grid search, random search, or Bayesian optimization to find the best set of hyperparameters.
  • Use early stopping to halt the training when the performance on a validation set starts to degrade.
  • Try different neural network architectures that might be better suited for your specific problem, such as convolutional neural networks (CNNs) for image data or recurrent neural networks (RNNs) for sequential data.
For more details on “plotregression” refer to the following link.


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