3/29/2021 0 Comments Entropy 0.9.2
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Not the answer youre looking for Browse other questions tagged bash unix or ask your own question. Here, we are going to discuss various performance metrics that can be used to evaluate predictions for classification problems. A confusion matrix is nothing but a table with two dimensions viz. Actual and Predicted and furthermore, both the dimensions have True Positives (TP), True Negatives (TN), False Positives (FP), False Negatives (FN) as shown below. We can easily calculate it by confusion matrix with the help of following formula. ![]() We can calculate F1 score with the help of following formula. In simple words, AUC-ROC metric will tell us about the capability of model in distinguishing the classes. Following is the graph showing ROC, AUC having TPR at y-axis and FPR at x-axis. It basically defined on probability estimates and measures the performance of a classification model where the input is a probability value between 0 and 1. It can be understood more clearly by differentiating it with accuracy. As we know that accuracy is the count of predictions (predicted value actual value) in our model whereas Log Loss is the amount of uncertainty of our prediction based on how much it varies from the actual label. ![]() We can use logloss function of sklearn.metrics to compute Log Loss. Here, we are going to discuss various performance metrics that can be used to evaluate predictions for regression problems. In simple words, with MAE, we can get an idea of how wrong the predictions were. ![]()
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