Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.inspection`.DecisionBoundaryDisplay

class sklearn.inspection.DecisionBoundaryDisplay(*, xx0, xx1, response, xlabel=None, ylabel=None)

  Decisions boundary visualization.

  It is recommended to use `from_estimator` to create a `DecisionBoundaryDisplay`. All parameters are stored as attributes.

  Read more in the User Guide.

  New in version 1.1.

  Parameters:
    xx0 ndarray of shape (grid_resolution, grid_resolution)

      First output of `meshgrid`.

    xx1 ndarray of shape (grid_resolution, grid_resolution)

      Second output of `meshgrid`.

    response ndarray of shape (grid_resolution, grid_resolution)

      Values of the response function.

    xlabel str, default=None

      Default label to place on x axis.

    ylabel str, default=None

      Default label to place on y axis.

  Attributes:
    surface_matplotlib `QuadContourSet` or `QuadMesh`

      If `plot_method` is ‘contour’ or ‘contourf’, `surface_` is a `QuadContourSet`. If `plot_method` is ‘pcolormesh’, `surface_` is a `QuadMesh`.

    ax_matplotlib Axes

      Axes with confusion matrix.

    figure_matplotlib Figure

      Figure containing the confusion matrix.

  See also

  `DecisionBoundaryDisplay.from_estimator`

    Plot decision boundary given an estimator.

  Examples

      >>> import matplotlib.pyplot as plt
      >>> import numpy as np
      >>> from sklearn.datasets import load_iris
      >>> from sklearn.inspection import DecisionBoundaryDisplay
      >>> from sklearn.tree import DecisionTreeClassifier
      >>> iris = load_iris()
      >>> feature_1, feature_2 = np.meshgrid(
      ...     np.linspace(iris.data[:, 0].min(), iris.data[:, 0].max()),
      ...     np.linspace(iris.data[:, 1].min(), iris.data[:, 1].max())
      ... )
      >>> grid = np.vstack([feature_1.ravel(), feature_2.ravel()]).T
      >>> tree = DecisionTreeClassifier().fit(iris.data[:, :2], iris.target)
      >>> y_pred = np.reshape(tree.predict(grid), feature_1.shape)
      >>> display = DecisionBoundaryDisplay(
      ...     xx0=feature_1, xx1=feature_2, response=y_pred
      ... )
      >>> display.plot()
      <...>
      >>> display.ax_.scatter(
      ...     iris.data[:, 0], iris.data[:, 1], c=iris.target, edgecolor="black"
      ... )
      <...>
      >>> plt.show()

  Methods

  `from_estimator`(estimator, X, *[, ...]) | Plot decision boundary given an estimator.
  `plot`([plot_method, ax, xlabel, ylabel]) | Plot visualization.

  classmethod from_estimator(estimator, X, *, grid_resolution=100, eps=1.0, plot_method='contourf', response_method='auto', xlabel=None, ylabel=None, ax=None, **kwargs)

    Plot decision boundary given an estimator.

    Read more in the User Guide.

    Parameters:
      estimator object

        Trained estimator used to plot the decision boundary.

      X{array-like, sparse matrix, dataframe} of shape (n_samples, 2)

        Input data that should be only 2-dimensional.

      grid_resolution int, default=100

        Number of grid points to use for plotting decision boundary. Higher values will make the plot look nicer but be slower to render.

      eps float, default=1.0

        Extends the minimum and maximum values of X for evaluating the response function.

      plot_method{‘contourf’, ‘contour’, ‘pcolormesh’}, default=’contourf’

        Plotting method to call when plotting the response. Please refer to the following matplotlib documentation for details: `contourf`, `contour`, `pcolormesh`.

      response_method{‘auto’, ‘predict_proba’, ‘decision_function’, ‘predict’}, default=’auto’

        Specifies whether to use predict_proba, decision_function, predict as the target response. If set to ‘auto’, the response method is tried in the following order: decision_function, predict_proba, predict. For multiclass problems, predict is selected when `response_method="auto"`.

      xlabel str, default=None

        The label used for the x-axis. If `None`, an attempt is made to extract a label from `X` if it is a dataframe, otherwise an empty string is used.

      ylabel str, default=None

        The label used for the y-axis. If `None`, an attempt is made to extract a label from `X` if it is a dataframe, otherwise an empty string is used.

      ax Matplotlib axes, default=None

        Axes object to plot on. If `None`, a new figure and axes is created.

      **kwargs dict

        Additional keyword arguments to be passed to the `plot_method`.

    Returns:
      display `DecisionBoundaryDisplay`

        Object that stores the result.

    See also

    `DecisionBoundaryDisplay`

      Decision boundary visualization.

    `sklearn.metrics.ConfusionMatrixDisplay.from_estimator`

      Plot the confusion matrix given an estimator, the data, and the label.

    `sklearn.metrics.ConfusionMatrixDisplay.from_predictions`

      Plot the confusion matrix given the true and predicted labels.

    Examples

        >>> import matplotlib.pyplot as plt
        >>> from sklearn.datasets import load_iris
        >>> from sklearn.linear_model import LogisticRegression
        >>> from sklearn.inspection import DecisionBoundaryDisplay
        >>> iris = load_iris()
        >>> X = iris.data[:, :2]
        >>> classifier = LogisticRegression().fit(X, iris.target)
        >>> disp = DecisionBoundaryDisplay.from_estimator(
        ...     classifier, X, response_method="predict",
        ...     xlabel=iris.feature_names[0], ylabel=iris.feature_names[1],
        ...     alpha=0.5,
        ... )
        >>> disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k")
        <...>
        >>> plt.show()

  plot(plot_method='contourf', ax=None, xlabel=None, ylabel=None, **kwargs)

    Plot visualization.

    Parameters:
      plot_method{‘contourf’, ‘contour’, ‘pcolormesh’}, default=’contourf’

        Plotting method to call when plotting the response. Please refer to the following matplotlib documentation for details: `contourf`, `contour`, `pcolormesh`.

      ax Matplotlib axes, default=None

        Axes object to plot on. If `None`, a new figure and axes is created.

      xlabel str, default=None

        Overwrite the x-axis label.

      ylabel str, default=None

        Overwrite the y-axis label.

      **kwargs dict

        Additional keyword arguments to be passed to the `plot_method`.

    Returns:
      display: `DecisionBoundaryDisplay`

        Object that stores computed values.

## Examples using `sklearn.inspection.DecisionBoundaryDisplay`

IsolationForest example

IsolationForest example

Plot classification boundaries with different SVM Kernels

Plot classification boundaries with different SVM Kernels

## Examples using `sklearn.inspection.DecisionBoundaryDisplay.from_estimator`

Classifier comparison

Classifier comparison

Inductive Clustering

Inductive Clustering

Plot the decision surface of decision trees trained on the iris dataset

Plot the decision surface of decision trees trained on the iris dataset

IsolationForest example

IsolationForest example

Plot the decision boundaries of a VotingClassifier

Plot the decision boundaries of a VotingClassifier

Two-class AdaBoost

Two-class AdaBoost

Logistic Regression 3-class Classifier

Logistic Regression 3-class Classifier

Plot multi-class SGD on the iris dataset

Plot multi-class SGD on the iris dataset

Plot multinomial and One-vs-Rest Logistic Regression

Plot multinomial and One-vs-Rest Logistic Regression

Class Likelihood Ratios to measure classification performance

Class Likelihood Ratios to measure classification performance

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Nearest Centroid Classification

Nearest Centroid Classification

Nearest Neighbors Classification

Nearest Neighbors Classification

Importance of Feature Scaling

Importance of Feature Scaling

Plot classification boundaries with different SVM Kernels

Plot classification boundaries with different SVM Kernels

Plot different SVM classifiers in the iris dataset

Plot different SVM classifiers in the iris dataset

Plot the support vectors in LinearSVC

Plot the support vectors in LinearSVC

SVM with custom kernel

SVM with custom kernel

SVM: Maximum margin separating hyperplane

SVM: Maximum margin separating hyperplane

SVM: Separating hyperplane for unbalanced classes

SVM: Separating hyperplane for unbalanced classes