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# Plotting Cross-Validated Predictions¶

This example shows how to use
`cross_val_predict`

together with
`PredictionErrorDisplay`

to visualize prediction
errors.

We will load the diabetes dataset and create an instance of a linear regression model.

```
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
X, y = load_diabetes(return_X_y=True)
lr = LinearRegression()
```

`cross_val_predict`

returns an array of the
same size of `y`

where each entry is a prediction obtained by cross
validation.

```
from sklearn.model_selection import cross_val_predict
y_pred = cross_val_predict(lr, X, y, cv=10)
```

Since `cv=10`

, it means that we trained 10 models and each model was
used to predict on one of the 10 folds. We can now use the
`PredictionErrorDisplay`

to visualize the
prediction errors.

On the left axis, we plot the observed values \(y\) vs. the predicted values \(\hat{y}\) given by the models. On the right axis, we plot the residuals (i.e. the difference between the observed values and the predicted values) vs. the predicted values.

```
import matplotlib.pyplot as plt
from sklearn.metrics import PredictionErrorDisplay
fig, axs = plt.subplots(ncols=2, figsize=(8, 4))
PredictionErrorDisplay.from_predictions(
y,
y_pred=y_pred,
kind="actual_vs_predicted",
subsample=100,
ax=axs[0],
random_state=0,
)
axs[0].set_title("Actual vs. Predicted values")
PredictionErrorDisplay.from_predictions(
y,
y_pred=y_pred,
kind="residual_vs_predicted",
subsample=100,
ax=axs[1],
random_state=0,
)
axs[1].set_title("Residuals vs. Predicted Values")
fig.suptitle("Plotting cross-validated predictions")
plt.tight_layout()
plt.show()
```

It is important to note that we used
`cross_val_predict`

for visualization
purpose only in this example.

It would be problematic to
quantitatively assess the model performance by computing a single
performance metric from the concatenated predictions returned by
`cross_val_predict`

when the different CV folds vary by size and distributions.

It is recommended to compute per-fold performance metrics using:
`cross_val_score`

or
`cross_validate`

instead.

**Total running time of the script:** (0 minutes 0.178 seconds)

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