RandomForestRegressor model evaluation?

问题

I am new to Machine-learning and trying to understand the correct and suitable evaluation for RandomForestRegressor. I have mentioned below Regression metrics and understood these concepts.

I am not sure that Which metrics I can use the for RandomForestRegressor's evaluation. Can I use r2_score all the time after prediction ?

I am using sklearn packages.

Regression metrics
See the Regression metrics section of the user guide for further details.

metrics.explained_variance_score(y_true, y_pred)    Explained variance regression score function
metrics.max_error(y_true, y_pred)   max_error metric calculates the maximum residual error.
metrics.mean_absolute_error(y_true, y_pred) Mean absolute error regression loss
metrics.mean_squared_error(y_true, y_pred[, …]) Mean squared error regression loss
metrics.mean_squared_log_error(y_true, y_pred)  Mean squared logarithmic error regression loss
metrics.median_absolute_error(y_true, y_pred)   Median absolute error regression loss
metrics.r2_score(y_true, y_pred[, …])   R^2 (coefficient of determination) regression score function.

来源：https://stackoverflow.com/questions/56415178/randomforestregressor-model-evaluation