Accuracy in ML vocabulary is used mostly for Classification problem i.e. Count of correct prediction out of total.
In a common speaking language, it will mean the predictive correctness of the model esp. on test data.
My understanding is that it's same as Score which can be calculated simply as
I am assuming that you are using SciKit-Learn,
score method for DecisionTreeRegressor will return R-square coefficient.Offical Link
score(self, X, y[, sample_weight])
Return the coefficient of determination R^2 of the prediction.
What should you do -
You should calculate two metrics - R-square and MAE/MSE.
Reason being - for an end-user/business person, MAE would be useful e.g. saying that model's prediction will be ~250$ away from the correct value on an average.
Challenge with MAE/MSE is that it doesn't say if it is good model unless you have an idea of the underlying data. e.g. Creating two models on pricing data of 2 different city - Boston/Tokyo and the MSE is 1000$/$1500.
You can't conclude that the former is a better model from this data.
R-square helps here.
Adjusted R-square (Another regression metrics) - If your feature set is fixed, then you need not check this metrics. It was devised to fix an issue with R-square when the feature set is different for different models.
Snippet to get RMSE, R-square, Adjusted R-square
def reg_metrics(y_test, y_pred, X_train):
from sklearn.metrics import mean_squared_error, r2_score
rmse = np.sqrt(mean_squared_error(y_test,y_pred))
r2 = r2_score(y_test,y_pred)
# Scikit-learn doesn't have adjusted r-square, hence custom code
n = y_pred.shape
k = X_train.shape
adj_r_sq = 1 - (1 - r2)*(n-1)/(n-1-k)
print(rmse, r2, adj_r_sq)
Links to study -
Statistics by Jim