Update example plots

examples/plot_huggingface_model.py

@@ -2,14 +2,14 @@
 Using a Trained QRF Model via Hugging Face Hub
 ==============================================
 
-An example of downloading a trained quantile regression forest (QRF) model
-from Hugging Face Hub and using it to estimate new quantiles. Here, a QRF has
-been trained with default parameters on a train-test split of the California
-housing dataset and uploaded to Hugging Face Hub. The model is downloaded and
-inference is performed over several quantiles for each instance in the dataset.
-The estimates are visualized by the latitude and longitude of each instance.
-The model used is available on Hugging Face Hub:
-https://huggingface.co/quantile-forest/california-housing-example
+This example demonstrates how to download a trained quantile regression forest
+(QRF) model from Hugging Face Hub and use it to estimate new quantiles. In
+this scenario, a QRF has been trained with default parameters on a train-test
+split of the California housing dataset and uploaded to Hugging Face Hub. The
+model is downloaded, and inference is performed over several quantiles for
+each instance in the dataset. The estimates are visualized by the latitude and
+longitude of each instance. The model used is available on Hugging Face Hub
+`here <https://huggingface.co/quantile-forest/california-housing-example>`_.
 """
 
 import os
@@ -170,7 +170,6 @@ def fit_and_upload_model(token, repo_id, local_dir="./local_repo"):
     .melt(id_vars=["index"], var_name="quantile", value_name="value")
     .merge(X[["Latitude", "Longitude", "Population"]].reset_index(), on="index", how="right")
 )
-print(df)
 
 
 def plot_quantiles_by_latlon(df, quantiles):

examples/plot_predict_custom.py

@@ -2,12 +2,13 @@
 Computing User-Specified Functions with QRFs
 ============================================
 
-An example that demonstrates a way of extracting the empirical distribution
-from a quantile regression forest (QRF) for one or more samples in order to
-calculate a user-specified function of interest. While a QRF is designed to
-estimate quantiles from the empirical distribution calculated for each sample,
-in many cases it may be useful to use the empirical distribution to calculate
-other quantities of interest. Here, we calculate the ECDF for a test sample.
+This example demonstrates how to extract the empirical distribution from a
+quantile regression forest (QRF) for one or more samples to calculate a
+user-specified function of interest. While a QRF is designed to estimate
+quantiles from the empirical distribution calculated for each sample, it can
+also be useful to use this empirical distribution to calculate other
+quantities of interest. Here, we calculate the empirical cumulative
+distribution function (ECDF) for a test sample.
 """
 
 from itertools import chain

examples/plot_quantile_conformalized.py

@@ -2,15 +2,16 @@
 QRFs for Conformalized Quantile Regression
 ==========================================
 
-An example that demonstrates the use of a quantile regression forest (QRF) to
+This example demonstrates the use of a quantile regression forest (QRF) to
 construct reliable prediction intervals using conformalized quantile
-regression (CQR). CQR offers prediction intervals that attain valid coverage,
-while QRF may require additional calibration for reliable interval estimates.
-Notice that in this example, by using CQR we obtain a level of coverage (i.e.,
-percentage of samples that actaully fall within their prediction interval)
-that is generally closer to the target level. Adapted from `"Prediction
-intervals: Quantile Regression Forests" by Carl McBride Ellis
-<https://www.kaggle.com/code/carlmcbrideellis/prediction-intervals-quantile-regression-forests>`_.
+regression (CQR). CQR provides prediction intervals that attain valid
+coverage, whereas QRF may require additional calibration for reliable interval
+estimates. In this example, by using CQR, we achieve a level of coverage
+(i.e., the percentage of samples that actually fall within their prediction
+interval) that is generally closer to the target level. This example is
+adapted from `"Prediction intervals: Quantile Regression Forests"
+<https://www.kaggle.com/code/carlmcbrideellis/prediction-intervals-quantile-regression-forests>`_
+by Carl McBride Ellis.
 """
 
 import altair as alt

examples/plot_quantile_example.py

@@ -2,9 +2,9 @@
 Predicting with Quantile Regression Forests
 ===========================================
 
-An example that demonstrates the use of a quantile regression forest to
+This example demonstrates the use of a quantile regression forest (QRF) to
 predict a conditional median and prediction intervals. The example compares
-the predictions to a ground truth function used to generate noisy samples.
+the QRF predictions to a ground truth function used to generate noisy samples.
 """
 
 import altair as alt

examples/plot_quantile_extrapolation.py

@@ -2,16 +2,16 @@
 Extrapolation with Quantile Regression Forests
 ==============================================
 
-An example on a toy dataset that illustrates the prediction intervals produced
-by a quantile regression forest (QRF) on extrapolated data. QRFs do not
-intrinsically extrapolate outside of the bounds of the training data, an
-important limitation of the approach; notice that the extrapolated interval
-with a standard QRF fails to reliably cover values outside of those observed
-in the training set. To overcome this limitation, we can use a procedure known
-as Xtrapolation that can estimate the extrapolation bounds for samples that
-fall outside the range of the training data. Adapted from "Extrapolation-Aware
-Nonparametric Statistical Inference" by Niklas Pfister and Peter Bühlmann:
-https://arxiv.org/abs/2402.09758.
+This example uses a toy dataset to illustrate the prediction intervals
+produced by a quantile regression forest (QRF) on extrapolated data. QRFs do
+not intrinsically extrapolate outside the bounds of the training data, which
+is an important limitation of the approach. Notice that the extrapolated
+interval with a standard QRF fails to reliably cover values outside those
+observed in the training set. To overcome this limitation, we can use a
+procedure known as Xtrapolation, which can estimate the extrapolation bounds
+for samples that fall outside the range of the training data. This example is
+adapted from `"Extrapolation-Aware Nonparametric Statistical Inference"
+<https://arxiv.org/abs/2402.09758>`_ by Niklas Pfister and Peter Bühlmann.
 """
 
 import math

examples/plot_quantile_interpolation.py

@@ -2,13 +2,12 @@
 Comparing Quantile Interpolation Methods
 ========================================
 
-An example illustration of the interpolation methods that can be applied
-during prediction when the desired quantile lies between two data points. In
-this toy example, the forest estimator creates a single split that separates
-samples 1–3 and samples 4–5, with quantiles calculated separately for these
-two groups based on the actual sample values. The interpolation methods are
-used when a calculated quantile does not precisely correspond to one of the
-actual values.
+This example illustrates the interpolation methods that can be applied during
+prediction when the desired quantile lies between two data points. In this toy
+example, the forest estimator creates a single split that separates samples
+1–3 and samples 4–5, with quantiles calculated separately for these two groups
+based on the actual sample values. The interpolation methods are used when a
+calculated quantile does not precisely correspond to one of the actual values.
 """
 
 import altair as alt

examples/plot_quantile_intervals.py

@@ -2,10 +2,10 @@
 Quantile Regression Forests Prediction Intervals
 ================================================
 
-An example of how to use quantile regression forests to generate prediction
-intervals on the California Housing dataset. Inspired by Figure 3 of
-"Quantile Regression Forests" by Meinshausen:
-https://jmlr.org/papers/v7/meinshausen06a.html.
+This example demonstrates how to use quantile regression forests (QRF) to
+generate prediction intervals on the California Housing dataset. Inspired by
+Figure 3 of `"Quantile Regression Forests"
+<https://jmlr.org/papers/v7/meinshausen06a.html>`_ by Meinshausen.
 """
 
 import altair as alt

examples/plot_quantile_multioutput.py

@@ -2,11 +2,11 @@
 Multiple-Output Quantile Regression with QRFs
 =============================================
 
-An example on a toy dataset that demonstrates fitting a single quantile
-regressor for multiple target variables. For each target, multiple quantiles
-can be estimated simultaneously. In this example, the target variable has
-two output values for each sample, with a single regressor used to estimate
-three quantiles (the median and interval points) for each target.
+This example demonstrates fitting a single quantile regressor for multiple
+target variables on a toy dataset. For each target, multiple quantiles can be
+estimated simultaneously. In this example, the target variable has two output
+values for each sample, with a single regressor used to estimate three
+quantiles (the median and interval points) for each target.
 """
 
 import altair as alt

examples/plot_quantile_vs_standard.py

@@ -2,8 +2,8 @@
 Quantile Regression Forests vs. Random Forests
 ==============================================
 
-An example comparison between the estimates generated by a quantile regression
-forest and a standard random forest regressor on a synthetic, right-skewed
+This example compares the estimates generated by a quantile regression forest
+(QRF) and a standard random forest regressor on a synthetic, right-skewed
 dataset. In a right-skewed distribution, the mean is to the right of the
 median. As illustrated by a greater overlap in the frequencies of the actual
 and predicted values, the median (quantile = 0.5) estimated by a quantile

examples/plot_treeshap_example.py

@@ -2,8 +2,8 @@
 Tree SHAP with Quantile Regression Forests
 ==========================================
 
-An example that demonstrates the use of SHAP (SHapley Additive exPlanations)
-to explain the predictions of a quantile regression forest (QRF) model. We
+This example demonstrates the use of SHAP (SHapley Additive exPlanations) to
+explain the predictions of a quantile regression forest (QRF) model. We
 generate a waterfall plot using the `Tree SHAP
 <https://shap.readthedocs.io/en/latest/generated/shap.TreeExplainer.html#shap.TreeExplainer>`_
 method to visualize the explanations for a single instance across multiple