Idiot’s Guide to Precision, Recall and Confusion Matrix
▻https://hackernoon.com/idiots-guide-to-precision-recall-and-confusion-matrix-b32d36463556?sourc
Evaluation metrics for classification modelsBuilding Machine Learning models is fun, making sure we build the best ones is what makes a difference!Evaluating ML modelsRegression modelsRMSE is a good measure to evaluate how a machine learning model is performing.If RMSE is significantly higher in test set than training-set — There is a good chance model is overfitting.(Make sure train and test set are from same/similar distribution)What about Classification models?Guess what, evaluating a Classification model is not that simpleBut why?You must be wondering ‘Can’t we just use accuracy of the model as the holy grail metric?’Accuracy is very important, but it might not be the best metric all the time. Let’s look at why with an example -:Let’s say we are building a model which predicts if a bank (...)
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