scikit-learn Tutorials (1)

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

Datasets

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 iniris.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 pylab as pl 
>>> pl.imshow(digits.images[-1], cmap=pl.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[0], -1))

Estimators objects   预测模型

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.fit(data)

所有预测模型fit就是梯度下降迭代的过程

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.param11

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:

预测后的参数，就是theta

>>>

>>> estimator.estimated_param_