**accuracy**Measures the over all performance of the classification model in terms of fraction of predictions made by the classifier that are correct. Interpretation: Accuracy is the fraction of number of correct predictions out of all examples, where the best value is 1 and the worst is 0.

**classificationerror**Measures the overall error rate of the classification model in terms of fraction of predictions made by the classifier that are not correct. Interpretation: Classification Error Rate is the fraction of number of in correct predictions out of all examples, where the best value is 1 and the worst is 0.

**confusionmatrix**Computes confusion matrix for classifier's predictions. The row represents the actual label and the column represents the predicted label. Each cell represents how many examples are actually actual label but predicted as predicted label, where the best value: except left to right diagonal, all are zero, and worst value: left to right diagonal are zero.

**f1score**It measures the performance of a classification model in terms of the both precision and recall. F1 score can be interpreted (loosely) as the average of precision and recall, where the best value is 1 and the worst is 0.

**logloss**It measures the performance of a classification model where the output of the classification model is in terms of probability. It takes into account the uncertainty of the predictions based on how much it varies from the actual label. Best Value: 0 Worst Value: tends to infinity (it increases as the predicted probability value diverges from actual label).

**precision**It measures the performance of a classification model in terms of the classifier’s ability to not label a negative example as positive. Precision score can be interpreted as the probability that a positive prediction made by the classifier is positive, where the best value is 1 and the worst is 0.

**recall**It measures the performance of a classification model in terms of the classifier's ability to predict positive examples correctly out of all positive examples. Recall score can be interpreted as the probability that a randomly selected positive example is correctly identified by the classifier, where the best value is 1 and the worst is 0.

**explainedvariance**Computes Explained Variance Regression Score from the actual and predicted outputs. It represents the amount of variance explained by the model with respect to variance of the actual output, where the best value is 1 and the worst is 0.

**hmse**Computes Half Mean Squared Error between two vectors. It is half of the average error made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).

**mae**Computes Mean Absolute Error between two vectors. It is the average of the error (absolute error) made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).

**maxerror**Computes Maximum Absolute Deviation between two vectors. It is the maximum of the error made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).

**mse**Computes Mean Squared Error between two vectors. It is the average of the error (squared to get rid of negative sign) made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).

**r2**Computes the Coefficient of Determination, which is the proportion of variance in the dependent variable that is predictable from the independent variable(s), where the best value is 1 and the worst value goes towards -Infinity.

**rmse**Computes Root Mean Squared Error between two vectors. It is the average of the amount of mistakes made by regression model in prediction, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).

**rss**Computes Residual Sum of Squares between two vectors. It is the sum of the error (squared to get rid of negative sign) made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).