api reference

Author: mathias-von-ottenbreit

API_REFERENCE.md

@@ -53,7 +53,7 @@ Limits 1) the number of terms already in the model that can be considered as int
 Species the variance power for the "tweedie" ***family***.
 
 #### group_size_for_validation_group_mse (default = 100)
-APLR calculates a tuning metric, mean squared error for groups of observations in the validation set. This metric is provided by the method ***get_validation_group_mse()***. The metric may be useful for tuning ***tweedie_power*** and to some extent ***family*** or ***link_function***. The reasoning behind this is that while mean squared error (MSE) could be inappropriate for evaluating for example tweedie distributed responses, MSE is often appropriate for evaluating normally distributed data. The sum response of a group of observations is approximately normally distributed according to the Central Limit Theorem (CLT) if there are enough observations in the group, even if the response for an individual observation has a different probability distribution. Ideally, ***group_size_for_validation_group_mse*** should be large enough so that the Central Limit Theorem holds (at least 30, but the default of 100 is a safer choice). Also, the number of observations in the validation set should be substantially higher than ***group_size_for_validation_group_mse***.
+APLR calculates a tuning metric, mean squared error for groups of observations in the validation set. This metric is provided by the method ***get_validation_group_mse()***. The metric may be useful for tuning ***tweedie_power*** and to some extent ***family*** or ***link_function***. The reasoning behind this is that while mean squared error (MSE) could be inappropriate for evaluating goodness of fit on for example tweedie distributed data, MSE is often appropriate for evaluating normally distributed data. The mean response and mean prediction of a group of observations is approximately normally distributed according to the Central Limit Theorem (CLT) if there are enough observations in the group, even if individual observations are not normally distributed. Ideally, ***group_size_for_validation_group_mse*** should be large enough so that the Central Limit Theorem holds (at least 30, but the default of 100 is a safer choice). Also, the number of observations in the validation set should be substantially higher than ***group_size_for_validation_group_mse***.
 
 
 ## Method: fit(X:npt.ArrayLike, y:npt.ArrayLike, sample_weight:npt.ArrayLike = np.empty(0), X_names:List[str]=[], validation_set_indexes:List[int]=[])