Evaluating Model Accuracy on a testing data set for a DecisionTreeReegressor Model I am trying an exercise where I have been asked to "Evaluate each model accuracy on testing data set for a max_depth parameter value changing from 2 to 5".

The model here is DecisionTreeRegressor. I just wanted to know what is the metric for calculating the Accuracy for a DecisionTreeRegressor model. 

My understanding is that it's same as Score which can be calculated simply as regressor.score(X_test, Y_test)

Please let me know what should be used to calculate the Accuracy of the DecisionTreeRegressor Model.
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 asregressor.score(X_test, Y_test)


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

#https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics

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[0]
    k = X_train.shape[1]
    adj_r_sq = 1 - (1 - r2)*(n-1)/(n-1-k)

    print(rmse, r2, adj_r_sq)


Links to study -
Statistics by Jim
Wikipedia