Map Machine Learning Metric. AUC_micro computed by combining the true positives and false positives from each class. After we train our machine learning its important to understand how well our model has performed.
Automatically transform tracked data into a knowledge repository then share and discuss your work with colleagues. Distance metrics are a key part of several machine learning algorithms. You know the model is predicting at about an 86 accuracy because the predictions on your training test said so.
While UMAP can be used for standard unsupervised dimension reduction the algorithm offers significant flexibility allowing it to be extended to perform other tasks including making use of categorical label information to do supervised dimension reduction and even metric learning.
Some of the properties that make it a preferred metric are. 0 1 Supported metric names include AUC_macro the arithmetic mean of the AUC for each class. Mar 6 2018 7 min read AP Average precision is a popular metric in measuring the accuracy of object detectors like Faster R-CNN SSD etc. The mean average precision mAP or sometimes simply called AP thats a popular metric used to measure the performance of models performing documentinformation retrieval and object detection.
