docs: Remove jargon from code examples

Removes subjective jargon like "good" and "bad" from the model names in the documentation. The examples now use neutral, descriptive names like "Model A" and "Model B" for clarity and professionalism.

Author: google-labs-jules[bot]

README.md

@@ -33,7 +33,7 @@ import rtichoke as rk
 # For reproducibility
 np.random.seed(42)
 
-# Generate more realistic sample data for a "good" model
+# Generate more realistic sample data for a model
 # Probabilities for the positive class are generally higher
 probs_positive_class = np.random.rand(50) * 0.5 + 0.5  # High probabilities (0.5 to 1.0)
 probs_negative_class = np.random.rand(50) * 0.5       # Low probabilities (0.0 to 0.5)
@@ -43,8 +43,8 @@ probs_combined = np.concatenate([probs_positive_class, probs_negative_class])
 reals_combined = np.concatenate([np.ones(50), np.zeros(50)])
 
 shuffle_index = np.random.permutation(100)
-probs = {'My Model': probs_combined[shuffle_index]}
-reals = {'My Population': reals_combined[shuffle_index]}
+probs = {'Model A': probs_combined[shuffle_index]}
+reals = {'Population': reals_combined[shuffle_index]}
 
 
 # Create the ROC curve

docs/tutorials/getting_started.qmd

@@ -32,14 +32,14 @@ This is the simplest case, where you want to evaluate the performance of a singl
 For this, you provide `probs` with a single entry for your model and `reals` with a single entry for the corresponding outcomes.
 
 ```python
-# Generate realistic sample data for a "good" model
+# Generate realistic sample data for a model
 probs_positive_class = np.random.rand(50) * 0.5 + 0.5
 probs_negative_class = np.random.rand(50) * 0.5
 probs_combined = np.concatenate([probs_positive_class, probs_negative_class])
 reals_combined = np.concatenate([np.ones(50), np.zeros(50)])
 shuffle_index = np.random.permutation(100)
 
-probs_single = {"Good Model": probs_combined[shuffle_index]}
+probs_single = {"Model A": probs_combined[shuffle_index]}
 reals_single = {"Population": reals_combined[shuffle_index]}
 
 # Create a ROC curve
@@ -60,23 +60,23 @@ Often, you want to compare the performance of several different models on the *s
 For this, you provide `probs` with an entry for each model you want to compare. `reals` will still have a single entry, since the outcome data is the same for all models.
 
 ```python
-# Generate data for a "Good Model", a "Bad Model", and a "Random Guess"
-# The "Good Model" has a clearer separation of probabilities.
-good_probs_pos = np.random.rand(50) * 0.4 + 0.6  # 0.6 to 1.0
-good_probs_neg = np.random.rand(50) * 0.4       # 0.0 to 0.4
-good_probs = np.concatenate([good_probs_pos, good_probs_neg])
+# Generate data for two different models to compare.
+# Model A has a clearer separation of probabilities.
+model_a_probs_pos = np.random.rand(50) * 0.4 + 0.6  # 0.6 to 1.0
+model_a_probs_neg = np.random.rand(50) * 0.4       # 0.0 to 0.4
+model_a_probs = np.concatenate([model_a_probs_pos, model_a_probs_neg])
 
-# The "Bad Model" has more overlap.
-bad_probs_pos = np.random.rand(50) * 0.5 + 0.4  # 0.4 to 0.9
-bad_probs_neg = np.random.rand(50) * 0.5 + 0.1  # 0.1 to 0.6
-bad_probs = np.concatenate([bad_probs_pos, bad_probs_neg])
+# Model B has more overlap.
+model_b_probs_pos = np.random.rand(50) * 0.5 + 0.4  # 0.4 to 0.9
+model_b_probs_neg = np.random.rand(50) * 0.5 + 0.1  # 0.1 to 0.6
+model_b_probs = np.concatenate([model_b_probs_pos, model_b_probs_neg])
 
 reals_comparison_data = np.concatenate([np.ones(50), np.zeros(50)])
 shuffle_index_comp = np.random.permutation(100)
 
 probs_comparison = {
-    "Good Model": good_probs[shuffle_index_comp],
-    "Bad Model": bad_probs[shuffle_index_comp],
+    "Model A": model_a_probs[shuffle_index_comp],
+    "Model B": model_b_probs[shuffle_index_comp],
     "Random Guess": np.random.rand(100)
 }
 reals_comparison = {"Population": reals_comparison_data[shuffle_index_comp]}