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Abstract The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) was adopted in 1975 in an effort to manage the international biodiversity trade.
In this post, we will learn about these Important Classification Metrics: Accuracy Precision Recall confusion matrix log loss F1 Score F-beta Score Area Under the ROC Curve (AUC-ROC) Area Under the Precision-Recall Curve (AUC-PR) G-Mean Hamming Loss Focal Loss we will learn these, by taking the example of the Email Spam Classifier Accuracy is perhaps the most intuitive metric in classification tasks. It represents the proportion of correctly classified instances out of the total instances.
Standardization is the process of scaling the data to get the mean of the data as 0 and the Standard deviation as 1. So, the data will be in the standard Normal distribution. Normalization is the process of scaling the data such that data lies in the specified range usually in the range of 0 to 1. In the Optimization techniques like Gradient Descent, it is mathematically proven, that the Standardization of data helps to converge faster.
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