Scikit-Learn库使用(1)

安装条件：

• Python (>= 2.6 or >= 3.3),
• NumPy (>= 1.6.1),
• SciPy (>= 0.9).

安装流程(以Mac为例，在终端中输入下述命令):

pip install -U scikit-learn

安装完成后，可以测试Scikit Learn库。如下示例代码:

"""

====================================

Plotting Cross-Validated Predictions

====================================

This example shows how to use `cross_val_predict` to visualize prediction

errors.

"""

#-*- coding: utf-8 -*-

from sklearn import datasets
from sklearn.model_selection import cross_val_predict
from sklearn import linear_model
import matplotlib.pyplot as plt


lr = linear_model.LinearRegression()
boston = datasets.load_boston()
y = boston.target


# cross_val_predict returns an array of the same size as `y` where each entry
# is a prediction obtained by cross validation:
predicted = cross_val_predict(lr, boston.data, y, cv=10)


fig, ax = plt.subplots()
ax.scatter(y, predicted)
ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
ax.set_xlabel('Measured')

ax.set_ylabel('Predicted')

plt.show()

运行，然后结果如下: