Glossary

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There are currently 14 names in this directory beginning with the letter M.

Machine learing

Machine learning is data analytic methods that detect unknown patterns from large data to predict the outcomes of interest.

Machine learning model

See Predictive model

Machine learning process

See Predictive modeling

Margin of error

The margin of error is a measure of the uncertainty or variability in the estimates obtained from the sample.

Mean Absolute Percentage Error (MAPE)

MAPE is a common metric for association models. It measures the percentage difference between the predicted values and the actual values in a dataset.

Measurement of variable

See Variable scale

Measurement scale

See Variable scale

Misclassification Rate (MR)

MR is a measure of how often a model misclassifies (makes incorrect predictions) the target variable (including actual positives and actual negatives).

Model fit

Model fit measures how well the model fits the data.

Model fit metrics

Criteria used to evaluate model fit

Model performance

See Model fit

Model reliability

Model reliability refers to the internal consistency of a model. It means the model produces consistent results with different runs using different data sets.

Model selection and tuning

Ensemble models require careful selection and tuning of base learners and configurations. Different combinations of models and configurations can significantly impact the model performance.

Model validity

Model validity refers to the accuracy of the model. It means the model consistently produces an accurate prediction of the target variable.