Scikit-learn deals with learning information from one or more datasets that are represented as 2D arrays. They can be understood as a list of multi-dimensional observations. We say that the first axis of these arrays is the samples axis, while the second is the features axis.
A simple example shipped with the scikit: iris dataset
>>> from sklearn import datasets >>> iris = datasets.load_iris() >>> data = iris.data >>> data.shape (150, 4)
It is made of 150 observations of irises, each described by 4 features: their sepal and petal length and width, as detailed in
When the data is not initially in the
(n_samples, n_features) shape, it needs to be preprocessed in order to be used by scikit-learn.
An example of reshaping data would be the digits dataset
The digits dataset is made of 1797 8x8 images of hand-written digits
>>> digits = datasets.load_digits() >>> digits.images.shape (1797, 8, 8) >>> import matplotlib.pyplot as plt >>> plt.imshow(digits.images[-1], cmap=plt.cm.gray_r) <matplotlib.image.AxesImage object at ...>
To use this dataset with the scikit, we transform each 8x8 image into a feature vector of length 64
>>> data = digits.images.reshape((digits.images.shape, -1))
Fitting data: the main API implemented by scikit-learn is that of the
estimator. An estimator is any object that learns from data; it may be a classification, regression or clustering algorithm or a transformer that extracts/filters useful features from raw data.
All estimator objects expose a
fit method that takes a dataset (usually a 2-d array):
Estimator parameters: All the parameters of an estimator can be set when it is instantiated or by modifying the corresponding attribute:
>>> estimator = Estimator(param1=1, param2=2) >>> estimator.param1 1
Estimated parameters: When data is fitted with an estimator, parameters are estimated from the data at hand. All the estimated parameters are attributes of the estimator object ending by an underscore:
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