Statistical learning: the setting and the estimator object in scikit-learn


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 scikit-learn: iris dataset

>>> from sklearn import datasets
>>> iris = datasets.load_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 iris.DESCR.

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], 
<matplotlib.image.AxesImage object at ...>

To use this dataset with scikit-learn, we transform each 8x8 image into a feature vector of length 64


Estimators objects

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

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:

>>> estimator.estimated_param_