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Can any please guide about how many types of the graph other than ROC can be plotted to represent the performance of the machine learning classifier?

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  • $\begingroup$ Accuracy or performance? $\endgroup$ – sentence Jul 11 at 10:09
  • $\begingroup$ @sentence my bad, its performance. $\endgroup$ – Muneeb Jul 11 at 10:13
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The basics are:

  1. Precision-Recall Curve
  2. ROC Curve

Then, you can plot other features of the model to get insights about generalization, learning process, overfitting/underfitting, like accuracy vs epochs, loss versus epochs, to name a few.

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Another useful "graph" is the Validation curve. This will show you the difference between your training curve and you testing curve.

https://scikit-learn.org/stable/auto_examples/model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py

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