I am working with the
DecisionTreeRegressor and trying to understand how well the data fits the model. I calculated both
coefficient of determination. At a certain depth,
coefficient of determination has the value equal to
0.9918744073066561 but the value of RMSE is equal to
75.0025. I cannot understand this. The value of RMSE is quite large but the value of the coefficient of determination is close to 1.0. What does it really mean? Is the model/fit good enough?
from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import AdaBoostRegressor from sklearn.metrics import mean_squared_error def rmse(y_true, y_pred): return math.sqrt(mean_squared_error(y_true, y_pred)) sample_depth = np.linspace(1,40, num = 40, dtype=int) dt_score_list =  for index, depth in enumerate(sample_depth): boosted_regressor = AdaBoostRegressor(DecisionTreeRegressor(max_depth=depth), random_state=1) boosted_regressor.fit(X_train, y_train) dt_score_list.append(boosted_regressor.score(X_test, y_test)) print(boosted_regressor.score(X_test, y_test), rmse(y_test, boosted_regressor.predict(X_test)), depth)