Hi everyone, I am at the learning stage of these algorithms; currently, I am about to do project work of my learning path of data science, I could build all the regression models successfully but quite confused about the visualization part of these models like what measures to be visualised. So any suggestions could be appreciated and if possible please share the code in both python and R.
It could be good to view the learning curves showing how the error minimise on both the training and validation sets. If it would be fine to plot a scatter gram of your test points with colors differentiating different classes and a plot of your decision boundaries. You don't need to color the data points for the regression problem. Also for the classification problem, you could show your confusion matrix and from there compute specificity, sensitivity and F-score. You can as well plot your ROC
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