Update docstring

src/dumme/dumme.py

@@ -20,28 +20,26 @@
 # BaseEstimator has boilerplate for things like get_params, set_params, _validate_data
 class MixedEffectsModel(RegressorMixin, BaseEstimator):
     """
-    This is the core class to instantiate, train, and predict using a mixed effects random forest model.
-    It roughly adheres to the sklearn estimator API.
-    Note that the user must pass in an already instantiated fixed_effects_model that adheres to the
-    sklearn regression estimator API, i.e. must have a fit() and predict() method defined.
-
-    It assumes a data model of the form:
+    Scikit-learn compatbile implementation of a mixed-effects model of the form
 
     .. math::
 
         y = f(X) + b_i Z + e
 
-    * y is the target variable. The current code only supports regression for now, e.g. continuously varying scalar value
+    * y is the target variable. The current code only supports regression for
+      now, e.g. continuously varying scalar value
     * X is the fixed effect features. Assume p dimensional
     * f(.) is the nonlinear fixed effects mode, e.g. random forest
     * Z is the random effect features. Assume q dimensional.
     * e is iid noise ~N(0, sigma_e²)
     * i is the cluster index. Assume k clusters in the training.
-    * bi is the random effect coefficients. They are different per cluster i but are assumed to be drawn from the same distribution ~N(0, Sigma_b) where Sigma_b is learned from the data.
+    * bi is the random effect coefficients. They are different per cluster i but
+      are assumed to be drawn from the same distribution ~N(0, Sigma_b) where
+      Sigma_b is learned from the data.
 
     Args:
-        gll_early_stop_threshold (float): early stopping threshold on GLL improvement
-        max_iterations (int): maximum number of EM iterations to train
+        gll_early_stop_threshold (float): early stopping threshold on GLL
+        improvement max_iterations (int): maximum number of EM iterations
     """
 
     def __init__(