.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_model_selection_plot_learning_curve.py:
========================
Plotting Learning Curves
========================
In the first column, first row the learning curve of a naive Bayes classifier
is shown for the digits dataset. Note that the training score and the
cross-validation score are both not very good at the end. However, the shape
of the curve can be found in more complex datasets very often: the training
score is very high at the beginning and decreases and the cross-validation
score is very low at the beginning and increases. In the second column, first
row we see the learning curve of an SVM with RBF kernel. We can see clearly
that the training score is still around the maximum and the validation score
could be increased with more training samples. The plots in the second row
show the times required by the models to train with various sizes of training
dataset. The plots in the third row show how much time was required to train
the models for each training sizes.
.. image:: /auto_examples/model_selection/images/sphx_glr_plot_learning_curve_001.png
:class: sphx-glr-single-img
.. code-block:: default
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
def plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None,
n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):
"""
Generate 3 plots: the test and training learning curve, the training
samples vs fit times curve, the fit times vs score curve.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
title : string
Title for the chart.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
axes : array of 3 axes, optional (default=None)
Axes to use for plotting the curves.
ylim : tuple, shape (ymin, ymax), optional
Defines minimum and maximum yvalues plotted.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 5-fold cross-validation,
- integer, to specify the number of folds.
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is not a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.
Refer :ref:`User Guide ` for the various
cross-validators that can be used here.
n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary `
for more details.
train_sizes : array-like, shape (n_ticks,), dtype float or int
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.
(default: np.linspace(0.1, 1.0, 5))
"""
if axes is None:
_, axes = plt.subplots(1, 3, figsize=(20, 5))
axes[0].set_title(title)
if ylim is not None:
axes[0].set_ylim(*ylim)
axes[0].set_xlabel("Training examples")
axes[0].set_ylabel("Score")
train_sizes, train_scores, test_scores, fit_times, _ = \
learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,
train_sizes=train_sizes,
return_times=True)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
fit_times_mean = np.mean(fit_times, axis=1)
fit_times_std = np.std(fit_times, axis=1)
# Plot learning curve
axes[0].grid()
axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1,
color="g")
axes[0].plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
axes[0].plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Cross-validation score")
axes[0].legend(loc="best")
# Plot n_samples vs fit_times
axes[1].grid()
axes[1].plot(train_sizes, fit_times_mean, 'o-')
axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,
fit_times_mean + fit_times_std, alpha=0.1)
axes[1].set_xlabel("Training examples")
axes[1].set_ylabel("fit_times")
axes[1].set_title("Scalability of the model")
# Plot fit_time vs score
axes[2].grid()
axes[2].plot(fit_times_mean, test_scores_mean, 'o-')
axes[2].fill_between(fit_times_mean, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1)
axes[2].set_xlabel("fit_times")
axes[2].set_ylabel("Score")
axes[2].set_title("Performance of the model")
return plt
fig, axes = plt.subplots(3, 2, figsize=(10, 15))
X, y = load_digits(return_X_y=True)
title = "Learning Curves (Naive Bayes)"
# Cross validation with 100 iterations to get smoother mean test and train
# score curves, each time with 20% data randomly selected as a validation set.
cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
estimator = GaussianNB()
plot_learning_curve(estimator, title, X, y, axes=axes[:, 0], ylim=(0.7, 1.01),
cv=cv, n_jobs=4)
title = r"Learning Curves (SVM, RBF kernel, $\gamma=0.001$)"
# SVC is more expensive so we do a lower number of CV iterations:
cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
estimator = SVC(gamma=0.001)
plot_learning_curve(estimator, title, X, y, axes=axes[:, 1], ylim=(0.7, 1.01),
cv=cv, n_jobs=4)
plt.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 4.440 seconds)
**Estimated memory usage:** 11 MB
.. _sphx_glr_download_auto_examples_model_selection_plot_learning_curve.py:
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