Prediction Error Plot

A prediction error plot shows the actual targets from the dataset against the predicted values generated by our model. This allows us to see how much variance is in the model. Data scientists can diagnose regression models using this plot by comparing against the 45 degree line, where the prediction exactly matches the model.

Visualizer	`PredictionError`
Quick Method	`prediction_error()`
Models	Regression
Workflow	Model Evaluation

from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split

from yellowbrick.datasets import load_concrete
from yellowbrick.regressor import PredictionError

# Load a regression dataset
X, y = load_concrete()

# Create the train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Instantiate the linear model and visualizer
model = Lasso()
visualizer = PredictionError(model)

visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer
visualizer.score(X_test, y_test)  # Evaluate the model on the test data
visualizer.show()                 # Finalize and render the figure

(Source code, png, pdf)

Prediction Error plot on the Concrete dataset using a linear model

Quick Method

The same functionality can be achieved with the associated quick method prediction_error. This method will build the PredictionError object with the associated arguments, fit it, then (optionally) immediately show the visualization.

from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split

from yellowbrick.datasets import load_concrete
from yellowbrick.regressor import prediction_error

# Load a regression dataset
X, y = load_concrete()

# Create the train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Instantiate the linear model and visualizer
model = Lasso()
visualizer = prediction_error(model, X_train, y_train, X_test, y_test)

(Source code, png, pdf)

API Reference

Comparison of the predicted vs. actual values for regression problems

class yellowbrick.regressor.prediction_error.PredictionError(estimator, ax=None, shared_limits=True, bestfit=True, identity=True, alpha=0.75, is_fitted='auto', **kwargs)[source]

Bases: RegressionScoreVisualizer

The prediction error visualizer plots the actual targets from the dataset against the predicted values generated by our model(s). This visualizer is used to detect noise or heteroscedasticity along a range of the target domain.

Parameters

estimatora Scikit-Learn regressor: Should be an instance of a regressor, otherwise will raise a YellowbrickTypeError exception on instantiation. If the estimator is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by is_fitted.
axmatplotlib Axes, default: None: The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required).
shared_limitsbool, default: True: If shared_limits is True, the range of the X and Y axis limits will be identical, creating a square graphic with a true 45 degree line. In this form, it is easier to diagnose under- or over- prediction, though the figure will become more sparse. To localize points, set shared_limits to False, but note that this will distort the figure and should be accounted for during analysis.
bestfitbool, default: True: Draw a linear best fit line to estimate the correlation between the predicted and measured value of the target variable. The color of the bestfit line is determined by the line_color argument.
identitybool, default: True: Draw the 45 degree identity line, y=x in order to better show the relationship or pattern of the residuals. E.g. to estimate if the model is over- or under- estimating the given values. The color of the identity line is a muted version of the line_color argument.
alphafloat, default: 0.75: Specify a transparency where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible.
is_fittedbool or str, default=’auto’: Specify if the wrapped estimator is already fitted. If False, the estimator will be fit when the visualizer is fit, otherwise, the estimator will not be modified. If ‘auto’ (default), a helper method will check if the estimator is fitted before fitting it again.
kwargsdict: Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers.

Notes

PredictionError is a ScoreVisualizer, meaning that it wraps a model and its primary entry point is the score() method.

Examples

>>> from yellowbrick.regressor import PredictionError
>>> from sklearn.linear_model import Lasso
>>> model = PredictionError(Lasso())
>>> model.fit(X_train, y_train)
>>> model.score(X_test, y_test)
>>> model.show()

Attributes

score_float: The R^2 score that specifies the goodness of fit of the underlying regression model to the test data.

draw(y, y_pred)[source]

Parameters

yndarray or Series of length n: An array or series of target or class values
y_predndarray or Series of length n: An array or series of predicted target values

Returns

axmatplotlib Axes: The axis with the plotted figure

finalize(**kwargs)[source]

Finalizes the figure by ensuring the aspect ratio is correct and adding the identity line for comparison. Also adds a title, axis labels, and the legend.

Parameters

kwargs: generic keyword arguments.

Notes

Generally this method is called from show and not directly by the user.

score(X, y, **kwargs)[source]

The score function is the hook for visual interaction. Pass in test data and the visualizer will create predictions on the data and evaluate them with respect to the test values. The evaluation will then be passed to draw() and the result of the estimator score will be returned.

Parameters

Xarray-like: X (also X_test) are the dependent variables of test set to predict
yarray-like: y (also y_test) is the independent actual variables to score against

Returns

scorefloat

yellowbrick.regressor.prediction_error.prediction_error(estimator, X_train, y_train, X_test=None, y_test=None, ax=None, shared_limits=True, bestfit=True, identity=True, alpha=0.75, is_fitted='auto', show=True, **kwargs)[source]

Quickly plot a prediction error visualizer

Plot the actual targets from the dataset against the predicted values generated by our model(s).

This helper function is a quick wrapper to utilize the PredictionError ScoreVisualizer for one-off analysis.

Parameters

estimatorthe Scikit-Learn estimator (should be a regressor): Should be an instance of a regressor, otherwise will raise a YellowbrickTypeError exception on instantiation. If the estimator is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by is_fitted.
X_trainndarray or DataFrame of shape n x m: A feature array of n instances with m features the model is trained on. Used to fit the visualizer and also to score the visualizer if test splits are not directly specified.
y_trainndarray or Series of length n: An array or series of target or class values. Used to fit the visualizer and also to score the visualizer if test splits are not specified.
X_testndarray or DataFrame of shape n x m, default: None: An optional feature array of n instances with m features that the model is scored on if specified, using X_train as the training data.
y_testndarray or Series of length n, default: None: An optional array or series of target or class values that serve as actual labels for X_test for scoring purposes.
axmatplotlib Axes: The axes to plot the figure on.
shared_limitsbool, default: True: If shared_limits is True, the range of the X and Y axis limits will be identical, creating a square graphic with a true 45 degree line. In this form, it is easier to diagnose under- or over- prediction, though the figure will become more sparse. To localize points, set shared_limits to False, but note that this will distort the figure and should be accounted for during analysis.
bestfitbool, default: True: Draw a linear best fit line to estimate the correlation between the predicted and measured value of the target variable. The color of the bestfit line is determined by the line_color argument.
identity: bool, default: True: Draw the 45 degree identity line, y=x in order to better show the relationship or pattern of the residuals. E.g. to estimate if the model is over- or under- estimating the given values. The color of the identity line is a muted version of the line_color argument.
alphafloat, default: 0.75: Specify a transparency where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible.
is_fittedbool or str, default=’auto’: Specify if the wrapped estimator is already fitted. If False, the estimator will be fit when the visualizer is fit, otherwise, the estimator will not be modified. If ‘auto’ (default), a helper method will check if the estimator is fitted before fitting it again.
show: bool, default: True: If True, calls show(), which in turn calls plt.show() however you cannot call plt.savefig from this signature, nor clear_figure. If False, simply calls finalize()
kwargsdict: Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers.

Returns

axmatplotlib Axes: Returns the axes that the prediction error plot was drawn on.