What are useful evaluation metrics used in machine learning
For model evaluation there are different metrics based on your model:


Confusion matrix

Classification accuracy:
(TP + TN) / (TP + TN + FP + FN)
Error rate: 
(FP + FN) / (TP + TN + FP + FN)
Paired criteria

Precision: (or Positive predictive value)
proportion of predicted positives which are actual positive
TP / (TP + FP)
Recall: proportion of actual positives which are predicted positive
TP / (TP + FN)

Sensitivity: proportion of actual positives which are predicted positive
TP / (TP + FN)
Specificity: proportion of actual negative which are predicted negative
TN / (TN + FP)
True positive rate: proportion of actual positives which are predicted
positive 
TP / (TP + FN)
True negative rate: proportion of actual negative which are predicted
negative
TN / (TN + FP)
Positive likelihood: likelihood that a predicted positive is an actual positive 
sensitivity / (1 - specificity)
Negative likelihood: likelihood that a predicted negative is an actual
negative
(1 - sensitivity) / specificity
Combined criteria

BCR: Balanced Classification Rate
½ (TP / (TP + FN) + TN / (TN + FP))
BER: Balanced Error Rate, or HTER
Half Total Error Rate: 1 - BCR
F-measure harmonic mean between precision and recall 
2 (precision . recall) / (precision + recall)
Fβ-measure weighted harmonic mean between precision and recall
(1+β)2 TP / ((1+β)2 TP + β2 FN + FP)


  The harmonic mean between specificity
  and sensitivity is also often used and
  sometimes referred to as F-measure.

Youden's index: arithmetic mean

between sensitivity and specificity
sensitivity - (1 - specificity)
Matthews correlation: correlation between the actual and predicted 
(TP . TN – FP . FN) / ((TP+FP) (TP+FN) (TP + FP) (TN+FN)) ^ (1/2) 
comprised between -1 and 1 Discriminant power normalized likelihood index
sqrt(3) / π .
(log (sensitivity / (1 – specificity)) +
log (specificity / (1 - sensitivity)))
&lt;1 = poor, >3 = good, fair otherwise


You can find much more here. Also there are some explanations here and you can find useful code snippet from here which are implemented.