gamma值與loss
import numpy as np
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import cross_val_score
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.learning_curve import validation_curve
digits = load_digits()
X = digits.data
y = digits.target
#設(shè)定gamma的變化范圍
param_range = np.logspace(-6,-2.3,5)
train_loss,test_loss = validation_curve(
SVC(),X,y,param_name = 'gamma',param_range = param_range,
cv = 10,scoring = 'mean_squared_error')
train_loss_mean = -np.mean(train_loss,axis = 1)
test_loss_mean = -np.mean(test_loss,axis = 1)
plt.plot(param_range,train_loss_mean,'o-',color = 'r',label = 'training')
plt.plot(param_range,test_loss_mean,'o-',color = 'g',label = 'test')
plt.legend(loc = 'best')
plt.show()
gamma與loss
x-axis : gamma
y_axis: loss
其中也體現(xiàn)了過擬合:在training data上的過分好
