80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
"""
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====================================
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Plotting Cross-Validated Predictions
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====================================
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This example shows how to use
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:func:`~sklearn.model_selection.cross_val_predict` together with
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:class:`~sklearn.metrics.PredictionErrorDisplay` to visualize prediction
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errors.
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"""
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# %%
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# We will load the diabetes dataset and create an instance of a linear
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# regression model.
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from sklearn.datasets import load_diabetes
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from sklearn.linear_model import LinearRegression
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X, y = load_diabetes(return_X_y=True)
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lr = LinearRegression()
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# %%
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# :func:`~sklearn.model_selection.cross_val_predict` returns an array of the
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# same size of `y` where each entry is a prediction obtained by cross
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# validation.
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from sklearn.model_selection import cross_val_predict
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y_pred = cross_val_predict(lr, X, y, cv=10)
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# %%
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# Since `cv=10`, it means that we trained 10 models and each model was
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# used to predict on one of the 10 folds. We can now use the
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# :class:`~sklearn.metrics.PredictionErrorDisplay` to visualize the
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# prediction errors.
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#
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# On the left axis, we plot the observed values :math:`y` vs. the predicted
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# values :math:`\hat{y}` given by the models. On the right axis, we plot the
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# residuals (i.e. the difference between the observed values and the predicted
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# values) vs. the predicted values.
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import matplotlib.pyplot as plt
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from sklearn.metrics import PredictionErrorDisplay
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fig, axs = plt.subplots(ncols=2, figsize=(8, 4))
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PredictionErrorDisplay.from_predictions(
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y,
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y_pred=y_pred,
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kind="actual_vs_predicted",
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subsample=100,
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ax=axs[0],
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random_state=0,
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)
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axs[0].set_title("Actual vs. Predicted values")
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PredictionErrorDisplay.from_predictions(
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y,
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y_pred=y_pred,
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kind="residual_vs_predicted",
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subsample=100,
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ax=axs[1],
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random_state=0,
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)
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axs[1].set_title("Residuals vs. Predicted Values")
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fig.suptitle("Plotting cross-validated predictions")
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plt.tight_layout()
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plt.show()
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# %%
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# It is important to note that we used
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# :func:`~sklearn.model_selection.cross_val_predict` for visualization
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# purpose only in this example.
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#
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# It would be problematic to
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# quantitatively assess the model performance by computing a single
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# performance metric from the concatenated predictions returned by
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# :func:`~sklearn.model_selection.cross_val_predict`
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# when the different CV folds vary by size and distributions.
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#
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# It is recommended to compute per-fold performance metrics using:
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# :func:`~sklearn.model_selection.cross_val_score` or
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# :func:`~sklearn.model_selection.cross_validate` instead.
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