Read more in the User Guide for general information
about the visualization API and
detailed documentation regarding the learning
curve visualization.
Added in version 1.2.
Parameters:
train_sizesndarray of shape (n_unique_ticks,)
Numbers of training examples that has been used to generate the
learning curve.
train_scoresndarray of shape (n_ticks, n_cv_folds)
Scores on training sets.
test_scoresndarray of shape (n_ticks, n_cv_folds)
Scores on test set.
score_namestr, 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 "Negativescore" 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.
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:
estimatorobject type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
Xarray-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.
yarray-like of shape (n_samples,) or (n_samples, n_outputs) or None
Target relative to X for classification or regression;
None for unsupervised learning.
groupsarray-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_sizesarray-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.
cvint, 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,
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.
scoringstr or callable, default=None
The scoring method to use when calculating the learning curve. Options:
If the estimator supports incremental learning, this will be
used to speed up fitting for different training set sizes.
n_jobsint, 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_dispatchint 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’.
verboseint, default=0
Controls the verbosity: the higher, the more messages.
shufflebool, default=False
Whether to shuffle training data before taking prefixes of it
based on`train_sizes`.
random_stateint, 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_paramsdict, default=None
Parameters to pass to the fit method of the estimator.
axmatplotlib Axes, default=None
Axes object to plot on. If None, a new figure and axes is
created.
negate_scorebool, 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_namestr, 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 "Negativescore"
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.
Axes object to plot on. If None, a new figure and axes is
created.
negate_scorebool, 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_namestr, 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 "Negativescore"
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.