validation
The accuracy is the proportion of true results (both true positives and true negatives) in the population.
Adjusted Rand Index.
Adjusted Rand Index. Adjusted Rand Index assumes the generalized hyper-geometric distribution as the model of randomness. The adjusted Rand index has the maximum value 1, and its expected value is 0 in the case of random clusters. A larger adjusted Rand index means a higher agreement between two partitions. The adjusted Rand index is recommended for measuring agreement even when the partitions compared have different numbers of clusters.
The area under the curve (AUC).
The area under the curve (AUC). When using normalized units, the area under the curve is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one (assuming 'positive' ranks higher than 'negative').
Computes the confusion matrix.
The F-score (or F-measure) considers both the precision and the recall of the test to compute the score.
The F-score (or F-measure) considers both the precision and the recall of the test to compute the score. The precision p is the number of correct positive results divided by the number of all positive results, and the recall r is the number of correct positive results divided by the number of positive results that should have been returned.
The traditional or balanced F-score (F1 score) is the harmonic mean of precision and recall, where an F1 score reaches its best value at 1 and worst at 0.
Fall-out, false alarm rate, or false positive rate (FPR).
Fall-out, false alarm rate, or false positive rate (FPR). Fall-out is actually Type I error and closely related to specificity (1 - specificity).
The false discovery rate (FDR) is ratio of false positives to combined true and false positives, which is actually 1 - precision.
Mean absolute deviation error.
MCC is a correlation coefficient between prediction and actual values.
MCC is a correlation coefficient between prediction and actual values. It is considered as a balanced measure for binary classification, even in unbalanced data sets. It varies between -1 and +1. 1 when there is perfect agreement between ground truth and prediction, -1 when there is a perfect disagreement between ground truth and predictions. MCC of 0 means the model is not better then random.
Mean squared error.
Normalized mutual information (normalized by max(H(y1), H(y2)) between two clusterings.
The precision or positive predictive value (PPV) is ratio of true positives to combined true and false positives, which is different from sensitivity.
Rand index is defined as the number of pairs of objects that are either in the same group or in different groups in both partitions divided by the total number of pairs of objects.
Rand index is defined as the number of pairs of objects that are either in the same group or in different groups in both partitions divided by the total number of pairs of objects. The Rand index lies between 0 and 1. When two partitions agree perfectly, the Rand index achieves the maximum value 1. A problem with Rand index is that the expected value of the Rand index between two random partitions is not a constant. This problem is corrected by the adjusted Rand index.
In information retrieval area, sensitivity is called recall.
Root mean squared error.
Residual sum of squares.
Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a statistical measures of the performance of a binary classification test.
Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a statistical measures of the performance of a binary classification test. Sensitivity is the proportion of actual positives which are correctly identified as such.
Specificity or True Negative Rate is a statistical measures of the performance of a binary classification test.
Specificity or True Negative Rate is a statistical measures of the performance of a binary classification test. Specificity measures the proportion of negatives which are correctly identified.
Test a generic classifier.
Test a generic classifier. The accuracy will be measured and printed out on standard output.
training data.
test data.
a code block to return a classifier trained on the given data.
the trained classifier.
Test a generic classifier.
Test a generic classifier. The accuracy will be measured and printed out on standard output.
the type of training and test data.
training data.
training labels.
test data.
test data labels.
a code block to return a classifier trained on the given data.
the trained classifier.
Test a binary classifier.
Test a binary classifier. The accuracy, sensitivity, specificity, precision, F-1 score, F-2 score, and F-0.5 score will be measured and printed out on standard output.
training data.
test data.
a code block to return a classifier trained on the given data.
the trained classifier.
Test a binary classifier.
Test a binary classifier. The accuracy, sensitivity, specificity, precision, F-1 score, F-2 score, and F-0.5 score will be measured and printed out on standard output.
the type of training and test data.
training data.
training labels.
test data.
test data labels.
a code block to return a binary classifier trained on the given data.
the trained classifier.
Test a binary soft classifier.
Test a binary soft classifier. The accuracy, sensitivity, specificity, precision, F-1 score, F-2 score, F-0.5 score, and AUC will be measured and printed out on standard output.
training data.
test data.
a code block to return a binary classifier trained on the given data.
the trained classifier.
Test a binary soft classifier.
Test a binary soft classifier. The accuracy, sensitivity, specificity, precision, F-1 score, F-2 score, F-0.5 score, and AUC will be measured and printed out on standard output.
the type of training and test data.
training data.
training labels.
test data.
test data labels.
a code block to return a binary classifier trained on the given data.
the trained classifier.
Model validation.