updated documentation

Author: mathias-von-ottenbreit

API_REFERENCE.md

@@ -14,10 +14,10 @@ The learning rate. Must be greater than zero and not more than one. The higher t
 Used to randomly split training observations into training and validation if ***validation_set_indexes*** is not specified when fitting.
 
 #### family (default = "gaussian")
-Determines the loss function used. Allowed values are "gaussian", "binomial", "poisson", "gamma" and "tweedie". This is used together with ***link_function***.
+Determines the loss function used. Allowed values are "gaussian", "binomial", "poisson", "gamma" and "tweedie". This is used together with ***link_function***. Please note that the implementation of values other than "gaussian" is experimental.
 
 #### link_function (default = "identity")
-Determines how the linear predictor is transformed to predictions. Allowed values are "identity", "logit", "log", "inverse" and "tweedie". These are canonical link functions for the "gaussian", "binomial", "poisson", "gamma" and "tweedie" ***family*** respectively. Canonical links usually work fine given that the data is appropriate for the selected combination of ***family*** and ***link_function***. Other combinations of ***family*** and ***link_function*** may or may not work (the model may fit poorly to the data if the wrong combination is used).
+Determines how the linear predictor is transformed to predictions. Allowed values are "identity", "logit", "log", "inverse" and "tweedie". These are canonical link functions for the "gaussian", "binomial", "poisson", "gamma" and "tweedie" ***family*** respectively. Canonical links usually work fine given that the data is appropriate for the selected combination of ***family*** and ***link_function***. Other combinations of ***family*** and ***link_function*** may or may not work (the model may fit poorly to the data if the wrong combination is used). Please note that the implementation of values other than "identity" is experimental.
 
 #### n_jobs (default = 0)
 Multi-threading parameter. If ***0*** then uses all available cores for multi-threading. Any other positive integer specifies the number of cores to use (***1*** means single-threading).

examples/train_aplr_cross_validation.py

@@ -28,8 +28,8 @@
 
 #Training model
 param_grid = {"max_interaction_level":[0,1,2,3,100],"min_observations_in_split":[1, 20, 50, 100, 200]}
-family="gaussian" #other available families are binomial, poisson, gamma and tweedie
-link_function="identity" #other available link functions are logit, log, inverse and tweedie
+family="gaussian" #other available families are binomial, poisson, gamma and tweedie, but their implementation is experimental.
+link_function="identity" #other available link functions are logit, log, inverse and tweedie, but their implementation is experimental.
 grid_search_cv = GridSearchCV(APLRRegressor(random_state=random_state,verbosity=1,m=1000,v=0.1,family=family,link_function=link_function),param_grid,cv=5,n_jobs=4,scoring="neg_mean_squared_error")
 grid_search_cv.fit(data_train[predictors].values,data_train[response].values)
 best_model:APLRRegressor = grid_search_cv.best_estimator_

examples/train_aplr_validation.py

@@ -32,8 +32,8 @@
 best_validation_result=np.inf
 param_grid=ParameterGrid({"max_interaction_level":[0,1,2,3,100],"min_observations_in_split":[1, 20, 50, 100, 200]})
 bestmodel=None
-family="gaussian" #other available families are binomial, poisson, gamma and tweedie
-link_function="identity" #other available link functions are logit, log, inverse and tweedie
+family="gaussian" #other available families are binomial, poisson, gamma and tweedie, but their implementation is experimental.
+link_function="identity" #other available link functions are logit, log, inverse and tweedie, but their implementation is experimental.
 for params in param_grid:
     model = APLRRegressor(random_state=random_state,verbosity=3,m=1000,v=0.1,family=family,link_function=link_function,**params) 
     model.fit(data_train[predictors].values,data_train[response].values,X_names=predictors)