01 sklearn Plotting Cross-Validated Predictions

其实在sklearn里面已经内置的很多经典的数据，比如波士顿的放假，iris花的数据等等。

导入数据方法：

from sklearn import datasetsboston = datasets.load_boston()X = boston.datay = boston.target

下面是引入模型的方法：

from sklearn.model_selection import cross_val_predict#方法1
from sklearn import linear_model
lr = linear_model.LinearRegression()
#方法2
from sklearn.linear_model import LogisticRegressionlogreg=LogisticRegressionpredict = cross_val_predict(lr,X,y,cv=10)



下面是完整的代码：
"""====================================Plotting Cross-Validated Predictions====================================This example shows how to use `cross_val_predict` to visualize predictionerrors."""from sklearn import datasetsfrom sklearn.model_selection import cross_val_predictfrom sklearn import linear_modelimport matplotlib.pyplot as pltlr = 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()