I am using Python and I want to plot this Classification Report. Can anyone help me to plot these results by using any python visualization tool? I want to visualize precision, recall, f1, and overall accuracy. Please guide me!
precision recall f1-score support Actor 0.797 0.711 0.752 83 Cast 1.000 1.000 1.000 4 Director 0.857 0.667 0.750 9 Movie 0.695 0.795 0.742 83 Music 0.583 0.875 0.700 16 O 0.785 0.869 0.825 176 Plot 0.800 0.640 0.711 25 Scene 0.825 0.579 0.680 57 Writer 0.900 0.562 0.692 16 accuracy 0.768 469 macro avg 0.805 0.744 0.761 469 weighted avg 0.777 0.768 0.765 469
You can do it via Python as by following : https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report
and through MATLAB : https://au.mathworks.com/help/bioinfo/ref/classperf.html
check out yellowbrick, an useful extension of scikitlearn
https://www.scikit-yb.org/en/latest/
Dear Jibran Mir ,
Look the link, maybe useful.
https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report
Regards,
Shafagat
Hi Jibran Mir if you are a beginner, please, forget about Stackoverflow.
I forgot to include this link here you can use it but in second, use the one in Bold First above ; https://www.datacamp.com/onboarding
Proceed here; https://mubaris.com/posts/kmeans-clustering/
Scroll down to
Implementation using Python
I encourage you to read it completely since this is a great short complete tutorial, without any complication, you get calculation for linear regression K-Mean clustering phase, witch is training a neural network with self-targeting randomly the headaches for example, and then compile a graphics relevant from them.
I put the emphasis on this because, currently, Artificial Intelligence i am studying since a year almost, I build the Cloud and Neural Network, there is nothing that can be 100% accurate, witch mean precisely;
You get a rate of 98% fine, but 2% of chance of failure. In real life application, 2% is really big, imagine there is lucky people, who did win 2 time the lottery and massive amount. How come, 2% is 2% and 98% is not 100%,
In real life, 2% you can find it back to back, so you would have take down 2 person on 2. Since 98% must be calculated for each person. 98% to take a Decision regarding your Neural Network, is not satisfying.
From there, training before, during and after in parallel computation to output the gradient loss function, this is not a clear science but we are actively working on it.
I hope this will help, feel free to manage your own assessments while doing work around relational database and non relational, K-mean etc, and use of logic.
If you have a Microsoft account, you can use Microsoft https://notebooks.azure.com/ for a frame to use Python and any one like Anaconda, you must update them in your environment.
Best Regards, Armand B.
It's better for him to understand it, i would not recommend Python at all, but a YAML coding interface, and even there.
Try to use this code. It has all functions to evaluate the model.
https://github.com/ibrahim85/classification-plot
You can use the following functions as an example:
1) classification_report(test, predictions) 2) confusion_matrix(test, predictions)
You can use https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report
Regards,
You can use this link for better understanding how to plot classification report
https://stackoverflow.com/questions/39662398/scikit-learn-output-metrics-classification-report-into-csv-tab-delimited-format
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