Code Documentation: Metrics¶
mastml.metrics Module¶
This module contains a metrics class for construction and evaluation of various regression score metrics between true and model predicted data.
- Metrics:
- Class to construct and evaluate a list of regression metrics of interest. The full list of available metrics can be obtained from Metrics()._metric_zoo()
Functions¶
r2_score_adjusted(y_true, y_pred[, n_features]) |
Method that calculates the adjusted R^2 value |
r2_score_fitted(y_true, y_pred) |
Method that calculates the R^2 value |
r2_score_noint(y_true, y_pred) |
Method that calculates the R^2 value without fitting the y-intercept |
rmse_over_stdev(y_true, y_pred[, train_y]) |
Method that calculates the root mean squared error (RMSE) of a set of data, divided by the standard deviation of the training data set. |
root_mean_squared_error(y_true, y_pred) |
Method that calculates the root mean squared error (RMSE) |
Classes¶
LinearRegression(*[, fit_intercept, …]) |
Ordinary least squares Linear Regression. |
Metrics(metrics_list[, metrics_type]) |
Class containing access to a wide range of metrics from scikit-learn and a number of MAST-ML custom-written metrics |
Class Inheritance Diagram¶
