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sklearn.datasets
.load_digits¶
- sklearn.datasets.load_digits(*, n_class=10, return_X_y=False, as_frame=False)[source]¶
Load and return the digits dataset (classification).
Each datapoint is a 8x8 image of a digit.
Classes
10
Samples per class
~180
Samples total
1797
Dimensionality
64
Features
integers 0-16
This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
Read more in the User Guide.
- Parameters:
- n_classint, default=10
The number of classes to return. Between 0 and 10.
- return_X_ybool, default=False
If True, returns
(data, target)
instead of a Bunch object. See below for more information about thedata
andtarget
object.New in version 0.18.
- as_framebool, default=False
If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If
return_X_y
is True, then (data
,target
) will be pandas DataFrames or Series as described below.New in version 0.23.
- Returns:
- data
Bunch
Dictionary-like object, with the following attributes.
- data{ndarray, dataframe} of shape (1797, 64)
The flattened data matrix. If
as_frame=True
,data
will be a pandas DataFrame.- target: {ndarray, Series} of shape (1797,)
The classification target. If
as_frame=True
,target
will be a pandas Series.- feature_names: list
The names of the dataset columns.
- target_names: list
The names of target classes.
New in version 0.20.
- frame: DataFrame of shape (1797, 65)
Only present when
as_frame=True
. DataFrame withdata
andtarget
.New in version 0.23.
- images: {ndarray} of shape (1797, 8, 8)
The raw image data.
- DESCR: str
The full description of the dataset.
- (data, target)tuple if
return_X_y
is True A tuple of two ndarrays by default. The first contains a 2D ndarray of shape (1797, 64) with each row representing one sample and each column representing the features. The second ndarray of shape (1797) contains the target samples. If
as_frame=True
, both arrays are pandas objects, i.e.X
a dataframe andy
a series.New in version 0.18.
- data
Examples
To load the data and visualize the images:
>>> from sklearn.datasets import load_digits >>> digits = load_digits() >>> print(digits.data.shape) (1797, 64) >>> import matplotlib.pyplot as plt >>> plt.gray() >>> plt.matshow(digits.images[0]) <...> >>> plt.show()
Examples using sklearn.datasets.load_digits
¶
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A demo of K-Means clustering on the handwritten digits data
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Various Agglomerative Clustering on a 2D embedding of digits
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Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…
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Explicit feature map approximation for RBF kernels
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The Johnson-Lindenstrauss bound for embedding with random projections
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Balance model complexity and cross-validated score
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Comparing randomized search and grid search for hyperparameter estimation
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Custom refit strategy of a grid search with cross-validation
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Plotting Learning Curves and Checking Models’ Scalability
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Dimensionality Reduction with Neighborhood Components Analysis
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Compare Stochastic learning strategies for MLPClassifier
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Restricted Boltzmann Machine features for digit classification
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Pipelining: chaining a PCA and a logistic regression
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Selecting dimensionality reduction with Pipeline and GridSearchCV
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Label Propagation digits: Demonstrating performance