scikit-learn最简单的方法是使用pip安装，在此之前确定已经安装：
python
Numpy
Scipy
pip install -U scikit-learn
pip3 install -U scikit-learn
scikit-learn提供了一些标准数据，例如用于分类的iris和digits数据集，以及波士顿房价回归数据集。
加载示例数据集
from sklearn import datasets
iris = datasets.load_iris()
digits = datasets.load_digits()
以digits数据集为例，样本特征
>>> print(digits.data)
[[ 0. 0. 5. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 10. 0. 0.]
[ 0. 0. 0. ..., 16. 9. 0.]
...,
[ 0. 0. 1. ..., 6. 0. 0.]
[ 0. 0. 2. ..., 12. 0. 0.]
[ 0. 0. 10. ..., 12. 1. 0.]]
样本类别
>>> digits.target
array([0, 1, 2, ..., 8, 9, 8])
数据总是二维数组(n_sample,n_feature)
在digits数据集中，数据是8x8的图像
>>> digits.images[0]
array([[ 0., 0., 5., 13., 9., 1., 0., 0.],
[ 0., 0., 13., 15., 10., 15., 5., 0.],
[ 0., 3., 15., 2., 0., 11., 8., 0.],
[ 0., 4., 12., 0., 0., 8., 8., 0.],
[ 0., 5., 8., 0., 0., 9., 8., 0.],
[ 0., 4., 11., 0., 1., 12., 7., 0.],
[ 0., 2., 14., 5., 10., 12., 0., 0.],
[ 0., 0., 6., 13., 10., 0., 0., 0.]])
学习和预测
在scikit-learn中，分类的估计器是Python对象，它实现fix(X,y) 和predict(T) 等方法
以支持向量分类为例
>>> from sklearn import svm
>>> clf = svm.SVC(gamma=0.001, C=100.)
将估计器应用到训练集
>>> clf.fit(digits.data[:-1], digits.target[:-1])
SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
通过估计器预测
>>> clf.predict(digits.data[-1:])
模型持久化
可以通过Python的内置持久化模块将模型保存
>>> from sklearn import svm
>>> from sklearn import datasets
>>> clf = svm.SVC()
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
>>> clf.fit(X, y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
>>> import pickle
>>> s = pickle.dumps(clf)
>>> clf2 = pickle.loads(s)
>>> clf2.predict(X[0:1])
array([0])
>>> y[0]
0
或者是使用joblib持久化模型
>>> from sklearn.externals import joblib
>>> joblib.dump(clf, 'filename.pkl')
通过joblib加载模型
>>> clf = joblib.load('filename.pkl')
类型转换
除非特别指定，输入将被转换为float64
>>> import numpy as np
>>> from sklearn import random_projection
>>> rng = np.random.RandomState(0)
>>> X = rng.rand(10, 2000)
>>> X = np.array(X, dtype='float32')
>>> X.dtype
dtype('float32')
>>> transformer = random_projection.GaussianRandomProjection()
>>> X_new = transformer.fit_transform(X)
>>> X_new.dtype
dtype('float64')
再次训练和更新参数
估计器的参数可以通过sklearn.pipeline.Pipeline.set_params更新。
>>> import numpy as np
>>> from sklearn.svm import SVC
>>> rng = np.random.RandomState(0)
>>> X = rng.rand(100, 10)
>>> y = rng.binomial(1, 0.5, 100)
>>> X_test = rng.rand(5, 10)
>>> clf = SVC()
>>> clf.set_params(kernel='linear').fit(X, y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
>>> clf.predict(X_test)
array([1, 0, 1, 1, 0])
>>> clf.set_params(kernel='rbf').fit(X, y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
>>> clf.predict(X_test)
array([0, 0, 0, 1, 0])