Statistical Measures

Azure ML Evaluation results often include some statistical measures that need some explanation. 

Here is a brief summary:-

For Classifiers

Measure	Description
True Positive	A count of the number of positive outcomes that the algorithm predicted correctly (TP)
True Negative	A count of the number of negative outcomes that the algorithm predicted correctly (TN)
False Positive	A count of the number of positive outcomes that the algorithm predicted incorrectly (FP)
False Negative	A count of the number of negative outcomes that the algorithm predicted incorrectly (FN)
Precision	The proportion of predicted positives that are classified correctly: TP/(TP+FP)
Recall	The proportion of actual positives which are classified correctly: TP/(TP+FN)
Accuracy	The proportion of all values classified correctly: (TP+TN)/(TP+TN+FP+FN). Accuracy is not a reliable metric for the real performance of a classifier.
F1 Score	The F1 score is the harmonic mean of precision and recall: F1 = 2 * ((precision/recall)/(precision+recall)). The F1 Score is a good metric for the real performance of a classifier since it includes both precision and recall.
AUC	Area Under the Curve: This is the are under the Receiver Operating Characteristic (ROC) curve. This is the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance. The AUC is a good metric for the real performance of a classifier since it includes both precision and recall.

For Regression

Measure	Description
Negative Loglikelihood	The Negative Loglikelihood is a measure of the variance of the actual data from the predicted values. A regression model attempts to reach the lowest Negative Loglikelihood. A low value indicates a well trained model.
Mean Absolute Error	A low value indicates a well trained model.
Root Mean Squared Error	A low value indicates a well trained model.
Relative Absolute Error	A low value indicates a well trained model.
Relative Squared Error	A low value indicates a well trained model.
Coefficient of Determination (R2)	A statistical measure of how well the regression line approximates the real data points. The coefficient of determination ranges from 0 to 1. An R2 of 1 indicates that the regression line perfectly fits the data, but low values can be entirely normal.

For Clustering

It's not clear to me yet what best indicates a well trained Clustering model.

Measure	Description
Average Distance to Cluster Center	The average closeness of all points in a cluster to the centroid of that cluster.
Average Distance to Other Center	The average closeness of all points in a cluster to the centroid of all clusters.
Number of Points	The number of points in that cluster
Maximal Distance To Cluster Center	The sum of the distances between each point and the centroid of that point’s cluster.