diff --git a/examples/plot_mixed_data.py b/examples/plot_mixed_data.py
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+"""
+ShaRP for classification on large datasets with mixed data types
+================================================================
+
+This example showcases a more complex setting, where we will develop and interpret a
+classification model using a larger dataset with both categorical and continuous
+features.
+
+``sharp`` is designed to operate over the unprocessed input space, to ensure every
+"Frankenstein" point generated to compute feature contributions are plausible. This means
+that the function producing the scores (or class predictions) should take as input the
+raw dataset, and every preprocessing step leading to the black box predictions/scores
+should be included within it.
+
+We will start by downloading the German Credit dataset.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.datasets import fetch_openml
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
+from sklearn.compose import ColumnTransformer
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sharp import ShaRP
+
+sns.set()
+
+df = fetch_openml(data_id=31, parser="auto")["frame"]
+df.head(5)
+
+######################################################################
+# Split X and y (input and target) from `df` and split train and test:
+
+X = df.drop(columns="class")
+y = df["class"]
+
+categorical_features = X.dtypes.apply(
+    lambda dtype: isinstance(dtype, pd.CategoricalDtype)
+).values
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.1, random_state=42
+)
+
+#########################################################################################
+# Now we will set up model. Here, we will use a pipeline to combine all the preprocessing
+# steps. However, to use ``sharp``, it is also sufficient to pass any function
+# (containing all the preprocessing steps) that takes a numpy array as input and outputs
+# the model's predictions.
+
+transformer = ColumnTransformer(
+    transformers=[
+        ("onehot", OneHotEncoder(sparse_output=False), categorical_features),
+        ("minmax", MinMaxScaler(), ~categorical_features),
+    ],
+    remainder="passthrough",
+    n_jobs=-1,
+)
+classifier = LogisticRegression(random_state=42)
+model = make_pipeline(transformer, classifier)
+model.fit(X_train.values, y_train.values)
+
+#########################################################################################
+# We can now use ``sharp`` to explain our model's predictions! If we consider the dataset
+# to be too large, we have a few options to reduce computational complexity, such as
+# configuring the ``n_jobs`` parameter, setting a value on ``sample_size``, or setting
+# ``measure=unary``.
+
+xai = ShaRP(
+    qoi="flip",
+    target_function=model.predict,
+    measure="unary",
+    sample_size=None,
+    random_state=42,
+    n_jobs=-1,
+    verbose=1
+)
+xai.fit(X_test)
+
+unary_values = pd.DataFrame(xai.all(X_test), columns=X.columns)
+unary_values
+
+##############################################################
+# Finally, we can plot the mean contributions of each feature:
+
+fig, ax = plt.subplots()
+xai.plot.bar(unary_values.mean(), ax=ax)
+ax.set_ylim(bottom=0)
+ax.tick_params(labelrotation=90)
+fig.tight_layout()
+plt.show()