diff --git a/_sources/gallery/plot_predict_custom.rst.txt b/_sources/gallery/plot_predict_custom.rst.txt
index 61ecd87..1531927 100644
--- a/_sources/gallery/plot_predict_custom.rst.txt
+++ b/_sources/gallery/plot_predict_custom.rst.txt
@@ -119,9 +119,19 @@ this scenario, we compute the empirical cumulative distribution function
 
         # Slider for determining the sample index for which the custom function is being visualized.
         slider = alt.binding_range(min=min_idx, max=max_idx, step=1, name="Test Sample Index: ")
-        index_selection = alt.selection_point(value=0, bind=slider, fields=["index"])
+        index_selection = alt.selection_point(
+            value=0,
+            bind=slider,
+            empty=False,
+            fields=["index"],
+            on="click",
+            nearest=True,
+        )
 
+        color = alt.condition(index_selection, alt.value("#006aff"), alt.value("lightgray"))
+        opacity = alt.condition(index_selection, alt.value(1), alt.value(0.2))
         tooltip = [
+            alt.Tooltip("index:Q", title="Sample Index"),
             alt.Tooltip("y_val:Q", title="Response Value"),
             alt.Tooltip("proba:Q", title="Probability"),
         ]
@@ -132,6 +142,8 @@ this scenario, we compute the empirical cumulative distribution function
             .encode(
                 x=alt.X("y_val:Q", title="Response Value"),
                 y=alt.Y("proba:Q", title="Probability"),
+                color=color,
+                opacity=opacity,
                 tooltip=tooltip,
             )
         )
@@ -143,14 +155,19 @@ this scenario, we compute the empirical cumulative distribution function
                 x=alt.X("y_val:Q", title="Response Value"),
                 x2=alt.X2("y_val2:Q"),
                 y=alt.Y("proba:Q", title="Probability"),
+                color=color,
+                opacity=opacity,
                 tooltip=tooltip,
             )
         )
 
+        # Ensure the selected sample index values overlay the unselected values.
+        chart_base = circles + lines
+        chart_selected = (circles + lines).transform_filter(index_selection)
+
         chart = (
-            (circles + lines)
+            (chart_base + chart_selected)
             .add_params(index_selection)
-            .transform_filter(index_selection)
             .properties(
                 title="Empirical Cumulative Distribution Function (ECDF) Plot",
                 height=400,
@@ -263,9 +280,19 @@ this scenario, we compute the empirical cumulative distribution function
 
                 # Slider for determining the sample index for which the custom function is being visualized.
                 slider = alt.binding_range(min=min_idx, max=max_idx, step=1, name="Test Sample Index: ")
-                index_selection = alt.selection_point(value=0, bind=slider, fields=["index"])
+                index_selection = alt.selection_point(
+                    value=0,
+                    bind=slider,
+                    empty=False,
+                    fields=["index"],
+                    on="click",
+                    nearest=True,
+                )
 
+                color = alt.condition(index_selection, alt.value("#006aff"), alt.value("lightgray"))
+                opacity = alt.condition(index_selection, alt.value(1), alt.value(0.2))
                 tooltip = [
+                    alt.Tooltip("index:Q", title="Sample Index"),
                     alt.Tooltip("y_val:Q", title="Response Value"),
                     alt.Tooltip("proba:Q", title="Probability"),
                 ]
@@ -276,6 +303,8 @@ this scenario, we compute the empirical cumulative distribution function
                     .encode(
                         x=alt.X("y_val:Q", title="Response Value"),
                         y=alt.Y("proba:Q", title="Probability"),
+                        color=color,
+                        opacity=opacity,
                         tooltip=tooltip,
                     )
                 )
@@ -287,14 +316,19 @@ this scenario, we compute the empirical cumulative distribution function
                         x=alt.X("y_val:Q", title="Response Value"),
                         x2=alt.X2("y_val2:Q"),
                         y=alt.Y("proba:Q", title="Probability"),
+                        color=color,
+                        opacity=opacity,
                         tooltip=tooltip,
                     )
                 )
 
+                # Ensure the selected sample index values overlay the unselected values.
+                chart_base = circles + lines
+                chart_selected = (circles + lines).transform_filter(index_selection)
+
                 chart = (
-                    (circles + lines)
+                    (chart_base + chart_selected)
                     .add_params(index_selection)
-                    .transform_filter(index_selection)
                     .properties(
                         title="Empirical Cumulative Distribution Function (ECDF) Plot",
                         height=400,
diff --git a/_sources/gallery/plot_quantile_multioutput.rst.txt b/_sources/gallery/plot_quantile_multioutput.rst.txt
index f5476d1..8784ced 100644
--- a/_sources/gallery/plot_quantile_multioutput.rst.txt
+++ b/_sources/gallery/plot_quantile_multioutput.rst.txt
@@ -161,9 +161,9 @@ for each target: the median line and the area defined by the interval points.
             .add_params(interval_val, click)
             .configure_range(category=alt.RangeScheme(list(legend.values())))
             .properties(
+                title="Multi-target Predictions and Prediction Intervals on Toy Dataset",
                 height=400,
                 width=650,
-                title="Multi-target Predictions and Prediction Intervals on Toy Dataset",
             )
         )
 
@@ -315,9 +315,9 @@ for each target: the median line and the area defined by the interval points.
                     .add_params(interval_val, click)
                     .configure_range(category=alt.RangeScheme(list(legend.values())))
                     .properties(
+                        title="Multi-target Predictions and Prediction Intervals on Toy Dataset",
                         height=400,
                         width=650,
-                        title="Multi-target Predictions and Prediction Intervals on Toy Dataset",
                     )
                 )
 
diff --git a/_sources/gallery/plot_treeshap_example.rst.txt b/_sources/gallery/plot_treeshap_example.rst.txt
index cd8b1ce..c29b9dc 100644
--- a/_sources/gallery/plot_treeshap_example.rst.txt
+++ b/_sources/gallery/plot_treeshap_example.rst.txt
@@ -108,6 +108,7 @@ how the SHAP explanations vary with different quantile choices.
     X = X.iloc[perm]
     y = y.iloc[perm]
     y *= 100_000  # convert to dollars
+
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=random_state)
 
     qrf = RandomForestQuantileRegressor(random_state=random_state)
@@ -410,6 +411,7 @@ how the SHAP explanations vary with different quantile choices.
             X = X.iloc[perm]
             y = y.iloc[perm]
             y *= 100_000  # convert to dollars
+
             X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=random_state)
 
             qrf = RandomForestQuantileRegressor(random_state=random_state)
diff --git a/_static/_image_hashes.json b/_static/_image_hashes.json
index cce864f..7379c64 100644
--- a/_static/_image_hashes.json
+++ b/_static/_image_hashes.json
@@ -1 +1 @@
-{"plot_quantile_interpolation.png": "e490d90e837fe8ab6cd368dc5af69c39", "plot_predict_custom.png": "337ddedec1648e46bc1a1831d7d4e07c", "plot_quantile_extrapolation.png": "79982e108d80c6b9a29dffb7fb761fa2", "plot_quantile_multioutput.png": "814b6b00bfe0ee6f39c91fe1c5da3dc8", "plot_quantile_example.png": "37b7aa1346e4418c4e23f424a891dd1f", "plot_quantile_conformalized.png": "b0a13f4c4f5c4357e037f0bb87809124", "plot_quantile_intervals.png": "0c2ccc83500678ca048240bc1eaed3c9", "plot_quantile_vs_standard.png": "7d705af48c41c27e7e8b712d3404c80b", "plot_treeshap_example.png": "3c6268285ebf715aeba9c21e4d4161c1", "plot_proximity_counts.png": "506fe1fe5d9cab22dde4d26df44d4dca", "plot_quantile_ranks.png": "9148bdf85c4e3e27c9f8dd295c0bf49d", "plot_huggingface_model.png": "5e60b72cd961ba7a3909203bb3a28083"}
\ No newline at end of file
+{"plot_quantile_interpolation.png": "e490d90e837fe8ab6cd368dc5af69c39", "plot_predict_custom.png": "60ec2b76ada2addd7e96a742f02c5464", "plot_quantile_extrapolation.png": "79982e108d80c6b9a29dffb7fb761fa2", "plot_quantile_multioutput.png": "9281997371c80f5ae412c0cc8d71842b", "plot_quantile_example.png": "37b7aa1346e4418c4e23f424a891dd1f", "plot_quantile_conformalized.png": "b0a13f4c4f5c4357e037f0bb87809124", "plot_quantile_intervals.png": "0c2ccc83500678ca048240bc1eaed3c9", "plot_quantile_vs_standard.png": "7d705af48c41c27e7e8b712d3404c80b", "plot_treeshap_example.png": "370a91ac8caaa9d64cef36f0d226e577", "plot_proximity_counts.png": "506fe1fe5d9cab22dde4d26df44d4dca", "plot_quantile_ranks.png": "9148bdf85c4e3e27c9f8dd295c0bf49d", "plot_huggingface_model.png": "5e60b72cd961ba7a3909203bb3a28083"}
\ No newline at end of file
diff --git a/_static/plot_predict_custom-thumb.png b/_static/plot_predict_custom-thumb.png
index 2c36a97..63ddb1c 100644
Binary files a/_static/plot_predict_custom-thumb.png and b/_static/plot_predict_custom-thumb.png differ
diff --git a/_static/plot_predict_custom.png b/_static/plot_predict_custom.png
index 01e403b..3b485cf 100644
Binary files a/_static/plot_predict_custom.png and b/_static/plot_predict_custom.png differ
diff --git a/gallery/plot_predict_custom.html b/gallery/plot_predict_custom.html
index 0837904..ae7715e 100644
--- a/gallery/plot_predict_custom.html
+++ b/gallery/plot_predict_custom.html
@@ -419,7 +419,7 @@
   // embed when document is loaded, to ensure vega library is available
   // this works on all modern browsers, except IE8 and older
   document.addEventListener("DOMContentLoaded", function(event) {
-      var spec = {"config": {"view": {"continuousWidth": 300, "continuousHeight": 300}}, "layer": [{"mark": {"type": "circle", "color": "#006aff", "opacity": 1, "size": 50}, "encoding": {"tooltip": [{"field": "y_val", "title": "Response Value", "type": "quantitative"}, {"field": "proba", "title": "Probability", "type": "quantitative"}], "x": {"field": "y_val", "title": "Response Value", "type": "quantitative"}, "y": {"field": "proba", "title": "Probability", "type": "quantitative"}}, "name": "view_10"}, {"mark": {"type": "line", "color": "#006aff", "size": 2}, "encoding": {"tooltip": [{"field": "y_val", "title": "Response Value", "type": "quantitative"}, {"field": "proba", "title": "Probability", "type": "quantitative"}], "x": {"field": "y_val", "title": "Response Value", "type": "quantitative"}, "x2": {"field": "y_val2"}, "y": {"field": "proba", "title": "Probability", "type": "quantitative"}}}], "data": {"name": "data-dc513c58b145f0916f2b337e4edcf42b"}, "height": 400, "params": [{"name": "param_9", "select": {"type": "point", "fields": ["index"]}, "bind": {"input": "range", "max": 99.0, "min": 0.0, "name": "Test Sample Index: ", "step": 1}, "value": 0, "views": ["view_10"]}], "title": "Empirical Cumulative Distribution Function (ECDF) Plot", "transform": [{"filter": {"param": "param_9"}}], "width": 650, "$schema": "https://vega.github.io/schema/vega-lite/v5.20.1.json", "datasets": {"data-dc513c58b145f0916f2b337e4edcf42b": [{"y_val": 85.0, "y_val2": 109.0, "proba": 0.01, "index": 0}, {"y_val": 109.0, "y_val2": 131.0, "proba": 0.017, "index": 0}, {"y_val": 131.0, "y_val2": 167.0, "proba": 0.062, "index": 0}, {"y_val": 167.0, "y_val2": 178.0, "proba": 0.075, "index": 0}, {"y_val": 178.0, "y_val2": 200.0, "proba": 0.102, "index": 0}, {"y_val": 200.0, "y_val2": 201.0, "proba": 0.108, "index": 0}, {"y_val": 201.0, "y_val2": 202.0, "proba": 0.123, "index": 0}, {"y_val": 202.0, "y_val2": 225.0, "proba": 0.177, "index": 0}, {"y_val": 225.0, "y_val2": 232.0, "proba": 0.202, "index": 0}, {"y_val": 232.0, "y_val2": 233.0, "proba": 0.216, "index": 0}, {"y_val": 233.0, "y_val2": 243.0, "proba": 0.221, "index": 0}, {"y_val": 243.0, "y_val2": 257.0, "proba": 0.23, "index": 0}, {"y_val": 257.0, "y_val2": 258.0, "proba": 0.262, "index": 0}, {"y_val": 258.0, "y_val2": 259.0, "proba": 0.343, "index": 0}, {"y_val": 259.0, "y_val2": 261.0, "proba": 0.384, "index": 0}, {"y_val": 261.0, "y_val2": 263.0, "proba": 0.413, "index": 0}, {"y_val": 263.0, "y_val2": 265.0, "proba": 0.494, "index": 0}, {"y_val": 265.0, "y_val2": 270.0, "proba": 0.507, "index": 0}, {"y_val": 270.0, "y_val2": 274.0, "proba": 0.523, "index": 0}, {"y_val": 274.0, "y_val2": 275.0, "proba": 0.585, "index": 0}, {"y_val": 275.0, "y_val2": 277.0, "proba": 0.604, "index": 0}, {"y_val": 277.0, "y_val2": 279.0, "proba": 0.617, "index": 0}, {"y_val": 279.0, "y_val2": 280.0, "proba": 0.669, "index": 0}, {"y_val": 280.0, "y_val2": 281.0, "proba": 0.687, "index": 0}, {"y_val": 281.0, "y_val2": 288.0, "proba": 0.703, "index": 0}, {"y_val": 288.0, "y_val2": 293.0, "proba": 0.853, "index": 0}, {"y_val": 293.0, "y_val2": 311.0, "proba": 0.871, "index": 0}, {"y_val": 311.0, "y_val2": 336.0, "proba": 0.88, "index": 0}, {"y_val": 336.0, "y_val2": 341.0, "proba": 0.906, "index": 0}, {"y_val": 341.0, "y_val2": null, "proba": 1.0, "index": 0}, {"y_val": 128.0, "y_val2": 163.0, "proba": 0.014, "index": 1}, {"y_val": 163.0, "y_val2": 164.0, "proba": 0.087, "index": 1}, {"y_val": 164.0, "y_val2": 166.0, "proba": 0.098, "index": 1}, {"y_val": 166.0, "y_val2": 178.0, "proba": 0.115, "index": 1}, {"y_val": 178.0, "y_val2": 197.0, "proba": 0.131, "index": 1}, {"y_val": 197.0, "y_val2": 202.0, "proba": 0.144, "index": 1}, {"y_val": 202.0, "y_val2": 212.0, "proba": 0.155, "index": 1}, {"y_val": 212.0, "y_val2": 217.0, "proba": 0.166, "index": 1}, {"y_val": 217.0, "y_val2": 220.0, "proba": 0.174, "index": 1}, {"y_val": 220.0, "y_val2": 222.0, "proba": 0.219, "index": 1}, {"y_val": 222.0, "y_val2": 225.0, "proba": 0.234, "index": 1}, {"y_val": 225.0, "y_val2": 233.0, "proba": 0.259, "index": 1}, {"y_val": 233.0, "y_val2": 236.0, "proba": 0.31, "index": 1}, {"y_val": 236.0, "y_val2": 242.0, "proba": 0.32, "index": 1}, {"y_val": 242.0, "y_val2": 243.0, "proba": 0.41, "index": 1}, {"y_val": 243.0, "y_val2": 246.0, "proba": 0.434, "index": 1}, {"y_val": 246.0, "y_val2": 248.0, "proba": 0.44, "index": 1}, {"y_val": 248.0, "y_val2": 249.0, "proba": 0.516, "index": 1}, {"y_val": 249.0, "y_val2": 252.0, "proba": 0.523, "index": 1}, {"y_val": 252.0, "y_val2": 258.0, "proba": 0.58, "index": 1}, {"y_val": 258.0, "y_val2": 262.0, "proba": 0.638, "index": 1}, {"y_val": 262.0, "y_val2": 268.0, "proba": 0.662, "index": 1}, {"y_val": 268.0, "y_val2": 272.0, "proba": 0.689, "index": 1}, {"y_val": 272.0, "y_val2": 273.0, "proba": 0.734, "index": 1}, {"y_val": 273.0, "y_val2": 275.0, "proba": 0.738, "index": 1}, {"y_val": 275.0, "y_val2": 277.0, "proba": 0.749, "index": 1}, {"y_val": 277.0, "y_val2": 281.0, "proba": 0.765, "index": 1}, {"y_val": 281.0, "y_val2": 295.0, "proba": 0.831, "index": 1}, {"y_val": 295.0, "y_val2": 296.0, "proba": 0.871, "index": 1}, {"y_val": 296.0, "y_val2": 306.0, "proba": 0.879, "index": 1}, {"y_val": 306.0, "y_val2": 308.0, "proba": 0.91, "index": 1}, {"y_val": 308.0, "y_val2": 310.0, "proba": 0.95, "index": 1}, {"y_val": 310.0, "y_val2": null, "proba": 1.0, "index": 1}, {"y_val": 60.0, "y_val2": 63.0, "proba": 0.014, "index": 2}, {"y_val": 63.0, "y_val2": 66.0, "proba": 0.019, "index": 2}, {"y_val": 66.0, "y_val2": 83.0, "proba": 0.036, "index": 2}, {"y_val": 83.0, "y_val2": 85.0, "proba": 0.047, "index": 2}, {"y_val": 85.0, "y_val2": 90.0, "proba": 0.058, "index": 2}, {"y_val": 90.0, "y_val2": 91.0, "proba": 0.066, "index": 2}, {"y_val": 91.0, "y_val2": 97.0, "proba": 0.075, "index": 2}, {"y_val": 97.0, "y_val2": 102.0, "proba": 0.096, "index": 2}, {"y_val": 102.0, "y_val2": 109.0, "proba": 0.109, "index": 2}, {"y_val": 109.0, "y_val2": 118.0, "proba": 0.135, "index": 2}, {"y_val": 118.0, "y_val2": 122.0, "proba": 0.346, "index": 2}, {"y_val": 122.0, "y_val2": 129.0, "proba": 0.356, "index": 2}, {"y_val": 129.0, "y_val2": 138.0, "proba": 0.358, "index": 2}, {"y_val": 138.0, "y_val2": 141.0, "proba": 0.401, "index": 2}, {"y_val": 141.0, "y_val2": 143.0, "proba": 0.42, "index": 2}, {"y_val": 143.0, "y_val2": 151.0, "proba": 0.429, "index": 2}, {"y_val": 151.0, "y_val2": 155.0, "proba": 0.447, "index": 2}, {"y_val": 155.0, "y_val2": 167.0, "proba": 0.475, "index": 2}, {"y_val": 167.0, "y_val2": 178.0, "proba": 0.498, "index": 2}, {"y_val": 178.0, "y_val2": 182.0, "proba": 0.597, "index": 2}, {"y_val": 182.0, "y_val2": 184.0, "proba": 0.618, "index": 2}, {"y_val": 184.0, "y_val2": 200.0, "proba": 0.624, "index": 2}, {"y_val": 200.0, "y_val2": 202.0, "proba": 0.665, "index": 2}, {"y_val": 202.0, "y_val2": 210.0, "proba": 0.699, "index": 2}, {"y_val": 210.0, "y_val2": 219.0, "proba": 0.706, "index": 2}, {"y_val": 219.0, "y_val2": 225.0, "proba": 0.718, "index": 2}, {"y_val": 225.0, "y_val2": 237.0, "proba": 0.841, "index": 2}, {"y_val": 237.0, "y_val2": 262.0, "proba": 0.848, "index": 2}, {"y_val": 262.0, "y_val2": 263.0, "proba": 0.855, "index": 2}, {"y_val": 263.0, "y_val2": 277.0, "proba": 0.865, "index": 2}, {"y_val": 277.0, "y_val2": 288.0, "proba": 0.892, "index": 2}, {"y_val": 288.0, "y_val2": 293.0, "proba": 0.971, "index": 2}, {"y_val": 293.0, "y_val2": 341.0, "proba": 0.992, "index": 2}, {"y_val": 341.0, "y_val2": null, "proba": 1.0, "index": 2}, {"y_val": 25.0, "y_val2": 31.0, "proba": 0.008, "index": 3}, {"y_val": 31.0, "y_val2": 42.0, "proba": 0.017, "index": 3}, {"y_val": 42.0, "y_val2": 47.0, "proba": 0.03, "index": 3}, {"y_val": 47.0, "y_val2": 50.0, "proba": 0.087, "index": 3}, {"y_val": 50.0, "y_val2": 51.0, "proba": 0.136, "index": 3}, {"y_val": 51.0, "y_val2": 52.0, "proba": 0.169, "index": 3}, {"y_val": 52.0, "y_val2": 53.0, "proba": 0.204, "index": 3}, {"y_val": 53.0, "y_val2": 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98}, {"y_val": 201.0, "y_val2": 202.0, "proba": 0.958, "index": 98}, {"y_val": 202.0, "y_val2": 210.0, "proba": 0.965, "index": 98}, {"y_val": 210.0, "y_val2": 246.0, "proba": 0.972, "index": 98}, {"y_val": 246.0, "y_val2": 265.0, "proba": 0.979, "index": 98}, {"y_val": 265.0, "y_val2": 277.0, "proba": 0.994, "index": 98}, {"y_val": 277.0, "y_val2": null, "proba": 1.0, "index": 98}, {"y_val": 51.0, "y_val2": 60.0, "proba": 0.007, "index": 99}, {"y_val": 60.0, "y_val2": 69.0, "proba": 0.015, "index": 99}, {"y_val": 69.0, "y_val2": 78.0, "proba": 0.058, "index": 99}, {"y_val": 78.0, "y_val2": 85.0, "proba": 0.078, "index": 99}, {"y_val": 85.0, "y_val2": 90.0, "proba": 0.113, "index": 99}, {"y_val": 90.0, "y_val2": 91.0, "proba": 0.142, "index": 99}, {"y_val": 91.0, "y_val2": 97.0, "proba": 0.199, "index": 99}, {"y_val": 97.0, "y_val2": 109.0, "proba": 0.233, "index": 99}, {"y_val": 109.0, "y_val2": 111.0, "proba": 0.274, "index": 99}, {"y_val": 111.0, "y_val2": 118.0, "proba": 0.3, "index": 99}, {"y_val": 118.0, "y_val2": 129.0, "proba": 0.321, "index": 99}, {"y_val": 129.0, "y_val2": 131.0, "proba": 0.364, "index": 99}, {"y_val": 131.0, "y_val2": 139.0, "proba": 0.379, "index": 99}, {"y_val": 139.0, "y_val2": 140.0, "proba": 0.392, "index": 99}, {"y_val": 140.0, "y_val2": 141.0, "proba": 0.525, "index": 99}, {"y_val": 141.0, "y_val2": 147.0, "proba": 0.582, "index": 99}, {"y_val": 147.0, "y_val2": 151.0, "proba": 0.634, "index": 99}, {"y_val": 151.0, "y_val2": 167.0, "proba": 0.667, "index": 99}, {"y_val": 167.0, "y_val2": 173.0, "proba": 0.793, "index": 99}, {"y_val": 173.0, "y_val2": 174.0, "proba": 0.802, "index": 99}, {"y_val": 174.0, "y_val2": 178.0, "proba": 0.812, "index": 99}, {"y_val": 178.0, "y_val2": 182.0, "proba": 0.842, "index": 99}, {"y_val": 182.0, "y_val2": 184.0, "proba": 0.852, "index": 99}, {"y_val": 184.0, "y_val2": 198.0, "proba": 0.861, "index": 99}, {"y_val": 198.0, "y_val2": 220.0, "proba": 0.89, "index": 99}, {"y_val": 220.0, "y_val2": 225.0, "proba": 0.898, "index": 99}, {"y_val": 225.0, "y_val2": 246.0, "proba": 0.911, "index": 99}, {"y_val": 246.0, "y_val2": 262.0, "proba": 0.928, "index": 99}, {"y_val": 262.0, "y_val2": 263.0, "proba": 0.935, "index": 99}, {"y_val": 263.0, "y_val2": 288.0, "proba": 0.975, "index": 99}, {"y_val": 288.0, "y_val2": null, "proba": 1.0, "index": 99}]}};
       var opt = {
         "mode": "vega-lite",
         "renderer": "canvas",
@@ -524,9 +524,19 @@
 
     <span class="c1"># Slider for determining the sample index for which the custom function is being visualized.</span>
     <span class="n">slider</span> <span class="o">=</span> <span class="n">alt</span><span class="o">.</span><span class="n">binding_range</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="n">min_idx</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">max_idx</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Test Sample Index: &quot;</span><span class="p">)</span>
-    <span class="n">index_selection</span> <span class="o">=</span> <span class="n">alt</span><span class="o">.</span><span class="n">selection_point</span><span class="p">(</span><span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">bind</span><span class="o">=</span><span class="n">slider</span><span class="p">,</span> <span class="n">fields</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;index&quot;</span><span class="p">])</span>
+    <span class="n">index_selection</span> <span class="o">=</span> <span class="n">alt</span><span class="o">.</span><span class="n">selection_point</span><span class="p">(</span>
+        <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="n">bind</span><span class="o">=</span><span class="n">slider</span><span class="p">,</span>
+        <span class="n">empty</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">fields</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;index&quot;</span><span class="p">],</span>
+        <span class="n">on</span><span class="o">=</span><span class="s2">&quot;click&quot;</span><span class="p">,</span>
+        <span class="n">nearest</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span>
 
+    <span class="n">color</span> <span class="o">=</span> <span class="n">alt</span><span class="o">.</span><span class="n">condition</span><span class="p">(</span><span class="n">index_selection</span><span class="p">,</span> <span class="n">alt</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="s2">&quot;#006aff&quot;</span><span class="p">),</span> <span class="n">alt</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="s2">&quot;lightgray&quot;</span><span class="p">))</span>
+    <span class="n">opacity</span> <span class="o">=</span> <span class="n">alt</span><span class="o">.</span><span class="n">condition</span><span class="p">(</span><span class="n">index_selection</span><span class="p">,</span> <span class="n">alt</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">alt</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="mf">0.2</span><span class="p">))</span>
     <span class="n">tooltip</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">alt</span><span class="o">.</span><span class="n">Tooltip</span><span class="p">(</span><span class="s2">&quot;index:Q&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Sample Index&quot;</span><span class="p">),</span>
         <span class="n">alt</span><span class="o">.</span><span class="n">Tooltip</span><span class="p">(</span><span class="s2">&quot;y_val:Q&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Response Value&quot;</span><span class="p">),</span>
         <span class="n">alt</span><span class="o">.</span><span class="n">Tooltip</span><span class="p">(</span><span class="s2">&quot;proba:Q&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Probability&quot;</span><span class="p">),</span>
     <span class="p">]</span>
@@ -537,6 +547,8 @@
         <span class="o">.</span><span class="n">encode</span><span class="p">(</span>
             <span class="n">x</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">X</span><span class="p">(</span><span class="s2">&quot;y_val:Q&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Response Value&quot;</span><span class="p">),</span>
             <span class="n">y</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Y</span><span class="p">(</span><span class="s2">&quot;proba:Q&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Probability&quot;</span><span class="p">),</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
+            <span class="n">opacity</span><span class="o">=</span><span class="n">opacity</span><span class="p">,</span>
             <span class="n">tooltip</span><span class="o">=</span><span class="n">tooltip</span><span class="p">,</span>
         <span class="p">)</span>
     <span class="p">)</span>
@@ -548,14 +560,19 @@
             <span class="n">x</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">X</span><span class="p">(</span><span class="s2">&quot;y_val:Q&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Response Value&quot;</span><span class="p">),</span>
             <span class="n">x2</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">X2</span><span class="p">(</span><span class="s2">&quot;y_val2:Q&quot;</span><span class="p">),</span>
             <span class="n">y</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">Y</span><span class="p">(</span><span class="s2">&quot;proba:Q&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Probability&quot;</span><span class="p">),</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
+            <span class="n">opacity</span><span class="o">=</span><span class="n">opacity</span><span class="p">,</span>
             <span class="n">tooltip</span><span class="o">=</span><span class="n">tooltip</span><span class="p">,</span>
         <span class="p">)</span>
     <span class="p">)</span>
 
+    <span class="c1"># Ensure the selected sample index values overlay the unselected values.</span>
+    <span class="n">chart_base</span> <span class="o">=</span> <span class="n">circles</span> <span class="o">+</span> <span class="n">lines</span>
+    <span class="n">chart_selected</span> <span class="o">=</span> <span class="p">(</span><span class="n">circles</span> <span class="o">+</span> <span class="n">lines</span><span class="p">)</span><span class="o">.</span><span class="n">transform_filter</span><span class="p">(</span><span class="n">index_selection</span><span class="p">)</span>
+
     <span class="n">chart</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="p">(</span><span class="n">circles</span> <span class="o">+</span> <span class="n">lines</span><span class="p">)</span>
+        <span class="p">(</span><span class="n">chart_base</span> <span class="o">+</span> <span class="n">chart_selected</span><span class="p">)</span>
         <span class="o">.</span><span class="n">add_params</span><span class="p">(</span><span class="n">index_selection</span><span class="p">)</span>
-        <span class="o">.</span><span class="n">transform_filter</span><span class="p">(</span><span class="n">index_selection</span><span class="p">)</span>
         <span class="o">.</span><span class="n">properties</span><span class="p">(</span>
             <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Empirical Cumulative Distribution Function (ECDF) Plot&quot;</span><span class="p">,</span>
             <span class="n">height</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span>
diff --git a/gallery/plot_quantile_multioutput.html b/gallery/plot_quantile_multioutput.html
index c6d6aae..e6b758d 100644
--- a/gallery/plot_quantile_multioutput.html
+++ b/gallery/plot_quantile_multioutput.html
@@ -566,9 +566,9 @@
         <span class="o">.</span><span class="n">add_params</span><span class="p">(</span><span class="n">interval_val</span><span class="p">,</span> <span class="n">click</span><span class="p">)</span>
         <span class="o">.</span><span class="n">configure_range</span><span class="p">(</span><span class="n">category</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">RangeScheme</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">legend</span><span class="o">.</span><span class="n">values</span><span class="p">())))</span>
         <span class="o">.</span><span class="n">properties</span><span class="p">(</span>
+            <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Multi-target Predictions and Prediction Intervals on Toy Dataset&quot;</span><span class="p">,</span>
             <span class="n">height</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span>
             <span class="n">width</span><span class="o">=</span><span class="mi">650</span><span class="p">,</span>
-            <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Multi-target Predictions and Prediction Intervals on Toy Dataset&quot;</span><span class="p">,</span>
         <span class="p">)</span>
     <span class="p">)</span>
 
diff --git a/gallery/plot_treeshap_example.html b/gallery/plot_treeshap_example.html
index 38ff6dc..2664c35 100644
--- a/gallery/plot_treeshap_example.html
+++ b/gallery/plot_treeshap_example.html
@@ -512,6 +512,7 @@
 <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">perm</span><span class="p">]</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">perm</span><span class="p">]</span>
 <span class="n">y</span> <span class="o">*=</span> <span class="mi">100_000</span>  <span class="c1"># convert to dollars</span>
+
 <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
 
 <span class="n">qrf</span> <span class="o">=</span> <span class="n">RandomForestQuantileRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
diff --git a/searchindex.js b/searchindex.js
index 925ce21..c500d4c 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"API Reference": [[0, null]], "Comparing Quantile Interpolation Methods": [[8, null]], "Computing User-Specified Functions with QRFs": [[3, null]], "Developer\u2019s Guide": [[16, null]], "Development Dependencies": [[16, "development-dependencies"]], "Development Installation": [[16, "development-installation"]], "Documentation": [[16, "documentation"]], "ExtraTreesQuantileRegressor": [[14, null]], "Extrapolation with Quantile Regression Forests": [[7, null]], "Fitting a Model": [[21, "fitting-a-model"]], "Fitting and Predicting": [[21, null]], "General Examples": [[1, null]], "Getting Started": [[17, null]], "Installation": [[17, "installation"]], "Introduction": [[22, null]], "Making Predictions": [[21, "making-predictions"]], "Multi-target Quantile Regression with QRFs": [[10, null]], "Out-of-Bag Estimation": [[21, "out-of-bag-estimation"]], "Predicting with Quantile Regression Forests": [[6, null]], "Prerequisites": [[17, "prerequisites"]], "Proximity Counts": [[23, null]], "QRFs for Conformalized Quantile Regression": [[5, null]], "Quantile Forests": [[0, "quantile-forests"]], "Quantile Ranks": [[24, null]], "Quantile Regression Forests": [[22, "quantile-regression-forests"]], "Quantile Regression Forests Prediction Intervals": [[9, null]], "Quantile Regression Forests vs. Random Forests": [[12, null]], "Quantile Weighting": [[21, "quantile-weighting"]], "Random Forest Predictions": [[21, "random-forest-predictions"]], "RandomForestQuantileRegressor": [[15, null]], "References": [[19, null]], "Release Notes": [[20, null]], "Test and Coverage": [[16, "test-and-coverage"]], "Tree SHAP with Quantile Regression Forests": [[13, null]], "Troubleshooting": [[16, "troubleshooting"]], "User-Specified Functions": [[21, "user-specified-functions"]], "Using Proximity Counts to Identify Similar Samples": [[4, null]], "Using Quantile Ranks to Identify Potential Outliers": [[11, null]], "Using a Trained QRF Model via Hugging Face Hub": 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