update docstrings

lib/scholar/metrics/classification.ex

@@ -1262,8 +1262,10 @@ defmodule Scholar.Metrics.Classification do
   each class, from which the log loss is computed by averaging the negative log
   of the probability forecasted for the true class over a number of samples.
 
-  `y_true` should contain `num_classes` unique values, and the sum of `y_prob`
-  along axis 1 should be 1 to respect the law of total probability.
+  `y_true` should be a tensor of shape {num_samples} containing values
+  between 0 and num_classes - 1 (inclusive).
+  `y_prob` should be a tensor of shape {num_samples, num_classes} containing
+  predicted probability distributions over classes for each sample.
 
   ## Options
 
@@ -1320,7 +1322,7 @@ defmodule Scholar.Metrics.Classification do
         type: to_float_type(y_prob)
       )
 
-    y_true_onehot =
+    y_one_hot =
       y_true
       |> Nx.new_axis(1)
       |> Nx.broadcast({num_samples, num_classes})
@@ -1329,7 +1331,7 @@ defmodule Scholar.Metrics.Classification do
     y_prob = Nx.clip(y_prob, 0, 1)
 
     sample_loss =
-      Nx.multiply(y_true_onehot, y_prob)
+      Nx.multiply(y_one_hot, y_prob)
       |> Nx.sum(axes: [-1])
       |> Nx.log()
       |> Nx.negate()

lib/scholar/naive_bayes/complement.ex

@@ -94,7 +94,8 @@ defmodule Scholar.NaiveBayes.Complement do
 
   @doc """
   Fits a complement naive Bayes classifier. The function assumes that the targets `y` are integers
-  between 0 and `num_classes` - 1 (inclusive).
+  between 0 and `num_classes` - 1 (inclusive). Otherwise, those samples will not
+  contribute to `class_count`.
 
   ## Options