Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.model_selection`.LearningCurveDisplay

class sklearn.model_selection.LearningCurveDisplay(*, train_sizes, train_scores, test_scores, score_name=None)

  Learning Curve visualization.

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

  Read more in the User Guide for general information about the visualization API and detailed documentation regarding the learning curve visualization.

  New in version 1.2.

  Parameters:
    train_sizes ndarray of shape (n_unique_ticks,)

      Numbers of training examples that has been used to generate the learning curve.

    train_scores ndarray of shape (n_ticks, n_cv_folds)

      Scores on training sets.

    test_scores ndarray of shape (n_ticks, n_cv_folds)

      Scores on test set.

    score_name str, default=None

      The name of the score used in `learning_curve`. It will override the name inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise.

  Attributes:
    ax_matplotlib Axes

      Axes with the learning curve.

    figure_matplotlib Figure

      Figure containing the learning curve.

    errorbar_list of matplotlib Artist or None

      When the `std_display_style` is `"errorbar"`, this is a list of `matplotlib.container.ErrorbarContainer` objects. If another style is used, `errorbar_` is `None`.

    lines_list of matplotlib Artist or None

      When the `std_display_style` is `"fill_between"`, this is a list of `matplotlib.lines.Line2D` objects corresponding to the mean train and test scores. If another style is used, `line_` is `None`.

    fill_between_list of matplotlib Artist or None

      When the `std_display_style` is `"fill_between"`, this is a list of `matplotlib.collections.PolyCollection` objects. If another style is used, `fill_between_` is `None`.

  See also

  `sklearn.model_selection.learning_curve`

    Compute the learning curve.

  Examples

      >>> import matplotlib.pyplot as plt
      >>> from sklearn.datasets import load_iris
      >>> from sklearn.model_selection import LearningCurveDisplay, learning_curve
      >>> from sklearn.tree import DecisionTreeClassifier
      >>> X, y = load_iris(return_X_y=True)
      >>> tree = DecisionTreeClassifier(random_state=0)
      >>> train_sizes, train_scores, test_scores = learning_curve(
      ...     tree, X, y)
      >>> display = LearningCurveDisplay(train_sizes=train_sizes,
      ...     train_scores=train_scores, test_scores=test_scores, score_name="Score")
      >>> display.plot()
      <...>
      >>> plt.show()

  Methods

  `from_estimator`(estimator, X, y, *[, groups, ...]) | Create a learning curve display from an estimator.
  `plot`([ax, negate_score, score_name, ...]) | Plot visualization.

  classmethod from_estimator(estimator, X, y, *, groups=None, train_sizes=array([0.1, 0.33, 0.55, 0.78, 1.]), cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=None, pre_dispatch='all', verbose=0, shuffle=False, random_state=None, error_score=nan, fit_params=None, ax=None, negate_score=False, score_name=None, score_type='both', log_scale='deprecated', std_display_style='fill_between', line_kw=None, fill_between_kw=None, errorbar_kw=None)

    Create a learning curve display from an estimator.

    Read more in the User Guide for general information about the visualization API and detailed documentation regarding the learning curve visualization.

    Parameters:
      estimator object type that implements the “fit” and “predict” methods

        An object of that type which is cloned for each validation.

      X array-like of shape (n_samples, n_features)

        Training data, where `n_samples` is the number of samples and `n_features` is the number of features.

      y array-like of shape (n_samples,) or (n_samples, n_outputs) or None

        Target relative to X for classification or regression; None for unsupervised learning.

      groups array-like of shape (n_samples,), default=None

        Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., `GroupKFold`).

      train_sizes array-like of shape (n_ticks,), default=np.linspace(0.1, 1.0, 5)

        Relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class.

      cv int, cross-validation generator or an iterable, default=None

        Determines the cross-validation splitting strategy. Possible inputs for cv are:

        - None, to use the default 5-fold cross validation,

        - int, to specify the number of folds in a `(Stratified)KFold`,

        - CV splitter,

        - An iterable yielding (train, test) splits as arrays of indices.

        For int/None inputs, if the estimator is a classifier and `y` is either binary or multiclass, `StratifiedKFold` is used. In all other cases, `KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls.

        Refer User Guide for the various cross-validation strategies that can be used here.

      scoring str or callable, default=None

        A string (see The scoring parameter: defining model evaluation rules) or a scorer callable object / function with signature `scorer(estimator, X, y)` (see Defining your scoring strategy from metric functions).

      exploit_incremental_learning bool, default=False

        If the estimator supports incremental learning, this will be used to speed up fitting for different training set sizes.

      n_jobs int, default=None

        Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the different training and test sets. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

      pre_dispatch int or str, default=’all’

        Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The str can be an expression like ‘2*n_jobs’.

      verbose int, default=0

        Controls the verbosity: the higher, the more messages.

      shuffle bool, default=False

        Whether to shuffle training data before taking prefixes of it based on`train_sizes`.

      random_state int, RandomState instance or None, default=None

        Used when `shuffle` is True. Pass an int for reproducible output across multiple function calls. See Glossary.

      error_score‘raise’ or numeric, default=np.nan

        Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised.

      fit_params dict, default=None

        Parameters to pass to the fit method of the estimator.

      ax matplotlib Axes, default=None

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

      negate_score bool, default=False

        Whether or not to negate the scores obtained through `learning_curve`. This is particularly useful when using the error denoted by `neg_*` in `scikit-learn`.

      score_name str, default=None

        The name of the score used to decorate the y-axis of the plot. It will override the name inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise.

      score_type{“test”, “train”, “both”}, default=”both”

        The type of score to plot. Can be one of `"test"`, `"train"`, or `"both"`.

      log_scale bool, default=”deprecated”

        Whether or not to use a logarithmic scale for the x-axis.

        Deprecated since version 1.3: `log_scale` is deprecated in 1.3 and will be removed in 1.5. Use `display.ax_.xscale` and `display.ax_.yscale` instead.

      std_display_style{“errorbar”, “fill_between”} or None, default=”fill_between”

        The style used to display the score standard deviation around the mean score. If `None`, no representation of the standard deviation is displayed.

      line_kw dict, default=None

        Additional keyword arguments passed to the `plt.plot` used to draw the mean score.

      fill_between_kw dict, default=None

        Additional keyword arguments passed to the `plt.fill_between` used to draw the score standard deviation.

      errorbar_kw dict, default=None

        Additional keyword arguments passed to the `plt.errorbar` used to draw mean score and standard deviation score.

    Returns:
      display `LearningCurveDisplay`

        Object that stores computed values.

    Examples

        >>> import matplotlib.pyplot as plt
        >>> from sklearn.datasets import load_iris
        >>> from sklearn.model_selection import LearningCurveDisplay
        >>> from sklearn.tree import DecisionTreeClassifier
        >>> X, y = load_iris(return_X_y=True)
        >>> tree = DecisionTreeClassifier(random_state=0)
        >>> LearningCurveDisplay.from_estimator(tree, X, y)
        <...>
        >>> plt.show()

  plot(ax=None, *, negate_score=False, score_name=None, score_type='both', log_scale='deprecated', std_display_style='fill_between', line_kw=None, fill_between_kw=None, errorbar_kw=None)

    Plot visualization.

    Parameters:
      ax matplotlib Axes, default=None

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

      negate_score bool, default=False

        Whether or not to negate the scores obtained through `learning_curve`. This is particularly useful when using the error denoted by `neg_*` in `scikit-learn`.

      score_name str, default=None

        The name of the score used to decorate the y-axis of the plot. It will override the name inferred from the `scoring` parameter. If `score` is `None`, we use `"Score"` if `negate_score` is `False` and `"Negative score"` otherwise. If `scoring` is a string or a callable, we infer the name. We replace `_` by spaces and capitalize the first letter. We remove `neg_` and replace it by `"Negative"` if `negate_score` is `False` or just remove it otherwise.

      score_type{“test”, “train”, “both”}, default=”both”

        The type of score to plot. Can be one of `"test"`, `"train"`, or `"both"`.

      log_scale bool, default=”deprecated”

        Whether or not to use a logarithmic scale for the x-axis.

        Deprecated since version 1.3: `log_scale` is deprecated in 1.3 and will be removed in 1.5. Use `display.ax_.set_xscale` and `display.ax_.set_yscale` instead.

      std_display_style{“errorbar”, “fill_between”} or None, default=”fill_between”

        The style used to display the score standard deviation around the mean score. If None, no standard deviation representation is displayed.

      line_kw dict, default=None

        Additional keyword arguments passed to the `plt.plot` used to draw the mean score.

      fill_between_kw dict, default=None

        Additional keyword arguments passed to the `plt.fill_between` used to draw the score standard deviation.

      errorbar_kw dict, default=None

        Additional keyword arguments passed to the `plt.errorbar` used to draw mean score and standard deviation score.

    Returns:
      display `LearningCurveDisplay`

        Object that stores computed values.

## Examples using `sklearn.model_selection.LearningCurveDisplay`

Plotting Learning Curves and Checking Models’ Scalability

Plotting Learning Curves and Checking Models' Scalability

## Examples using `sklearn.model_selection.LearningCurveDisplay.from_estimator`

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2

Comparison of kernel ridge regression and SVR

Comparison of kernel ridge regression and SVR

Plotting Learning Curves and Checking Models’ Scalability

Plotting Learning Curves and Checking Models' Scalability