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What does a high log loss mean?
Log-loss is indicative of how close the prediction probability is to the corresponding actual/true value (0 or 1 in case of binary classification). The more the predicted probability diverges from the actual value, the higher is the log-loss value.
Is log loss a metric?
When it comes to a classification task, log loss is one of the most commonly used metrics. It is also known as the cross-entropy loss. Log loss (i.e. cross-entropy loss) evaluates the performance by comparing the actual class labels and the predicted probabilities. The comparison is quantified using cross-entropy.
What are the advantages of a confusion matrix?
Advantages of a confusion matrix: 1 The confusion matrix provides detailed results of the classification. 2 Derivates of the confusion matrix are widely used. 3 Visual inspection of results can be enhanced by using a heat map. More
When to use logloss instead of other loss functions?
In the biased cases, I understand that logloss has the same problem as the accuracy and other loss functions : it provides only a global measurement of your performance. So you would better complement your understanding with metrics focused on the minority classes (recall and precision), or maybe not use logloss at all.
How to add a label and percentage to a confusion matrix?
How to add a label and percentage to a confusion matrix plotted using a Seaborn heatmap. Plus some additional options. One great tool for evaluating the behavior and understanding the effectiveness of a binary or categorical classifier is the Confusion Matrix.
Why is it called the confusio n matrix?
Side note: I absolutely love the name confusio n matrix because it reminds me of Giosue Cozzarelli — the Panamanian beauty pageant contestant who, when asked to explain the Confucius quote: “ Reading without meditating is a useless occupation. ” proclaimed that “ Confucius was one of the men who invented confusion…