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, copy_X, ...])	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

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