# gist-score-svm

**Description:** Compute various statistics from svm output files.

**Usage:** gist-score-svm [options] <weights>

**Inputs:**

- weights - an SVM weights file produced by compute-weights

**Output:**

Number of training examples: 985 (477 positive, 508 negative)
Number of support vectors: 498 (217 positive, 281 negative) 50.6%
Training results: FP=0 FN=0 TP=477 TN=508
Training ROC: 1.00000
Test results: FP=103 FN=107 TP=370 TN=405
Test ROC: 0.85417

The first line of output reports the number of examples in the
training set.
The second line reports the number and percentage of those examples
that are support vectors (i.e., for which the weight is non-zero).
The third line will only be printed if the weights file specifies
constraints on the magnitudes of the support vector weights. This
line reports the number and percentage of support vectors whose
weights reached the set bounds. Note that the percentage reported on
this line is with respect to the entire data set, not the set of
support vectors.
The fourth line reports the number of false positive, false negative,
true positive and true negative classifications of the training set.
The fifth line reports the ROC score, which is the normalized area
under a curve that plots true positives as a function of false
positives for varying decision thresholds. Perfect classification
corresponds to an ROC of 1.0.
The sixth and seventh lines report the same values as the fourth and
fifth lines, but with respect to the test set. The test set
statistics may computed with respect to hold-one-out predictions if
the weights file contains them. Alternatively, the test set may be
supplied as additional files, using the `-test`

option. If
no test set is given, these two lines will not appear in the output.
**Options:**

- -test <truth> <predictions> - Compute the total
number of errors with respect to an external test set.
- -rdb - Assume that the input files contain RDB format lines.

**Calls:** none