你如何让sklearn's SGDClassifier
在预测中表现出不确定性?
我正在尝试确认SGDClassifier
将报告与任何标签不严格对应的输入数据的概率为50%。但是,我发现分类器始终是100%确定的。
我正在使用以下脚本对此进行测试:
from sklearn.linear_model import SGDClassifier
c = SGDClassifier(loss="log")
#c = SGDClassifier(loss="modified_huber")
X = [
# always -1
[1,0,0],
[1,0,0],
[1,0,0],
[1,0,0],
# always +1
[0,0,1],
[0,0,1],
[0,0,1],
[0,0,1],
# uncertain
[0,1,0],
[0,1,0],
[0,1,0],
[0,1,0],
[0,1,0],
[0,1,0],
[0,1,0],
[0,1,0],
]
y = [
-1,
-1,
-1,
-1,
+1,
+1,
+1,
+1,
-1,
+1,
-1,
+1,
-1,
+1,
-1,
+1,
]
def lookup_prob_class(c, dist):
a = sorted(zip(dist, c.classes_))
best_prob, best_class = a[-1]
return best_prob, best_class
c.fit(X, y)
probs = c.predict_proba(X)
print 'probs:'
for dist, true_value in zip(probs, y):
prob, value = lookup_prob_class(c, dist)
print '%.02f'%prob, value, true_value
如您所见,我的训练数据总是将-1与输入数据[1,0,0]相关联,+1与[0,0,1]相关联,并且[0,1,0]为50/50。
因此,我希望predict_proba()
的结果为输入[0,1,0]返回0.5。但相反,它报告的概率为100%。为什么会这样,我该如何解决?
有趣的是,为DecisionTreeClassifier
或RandomForestClassifier
换出SGDClassifier
会产生我期望的输出。
答案 0 :(得分:4)
确实显示出一些不确定性:
>>> c.predict_proba(X)
array([[ 9.97254333e-01, 2.74566740e-03],
[ 9.97254333e-01, 2.74566740e-03],
[ 9.97254333e-01, 2.74566740e-03],
[ 9.97254333e-01, 2.74566740e-03],
[ 1.61231111e-06, 9.99998388e-01],
[ 1.61231111e-06, 9.99998388e-01],
[ 1.61231111e-06, 9.99998388e-01],
[ 1.61231111e-06, 9.99998388e-01],
[ 1.24171982e-04, 9.99875828e-01],
[ 1.24171982e-04, 9.99875828e-01],
[ 1.24171982e-04, 9.99875828e-01],
[ 1.24171982e-04, 9.99875828e-01],
[ 1.24171982e-04, 9.99875828e-01],
[ 1.24171982e-04, 9.99875828e-01],
[ 1.24171982e-04, 9.99875828e-01],
[ 1.24171982e-04, 9.99875828e-01]])
如果您希望模型更加不确定,您必须更加强烈地规范它。这是通过调整alpha
参数:
>>> c = SGDClassifier(loss="log", alpha=1)
>>> c.fit(X, y)
SGDClassifier(alpha=1, class_weight=None, epsilon=0.1, eta0=0.0,
fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',
loss='log', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5,
random_state=None, shuffle=False, verbose=0, warm_start=False)
>>> c.predict_proba(X)
array([[ 0.58782817, 0.41217183],
[ 0.58782817, 0.41217183],
[ 0.58782817, 0.41217183],
[ 0.58782817, 0.41217183],
[ 0.53000442, 0.46999558],
[ 0.53000442, 0.46999558],
[ 0.53000442, 0.46999558],
[ 0.53000442, 0.46999558],
[ 0.55579239, 0.44420761],
[ 0.55579239, 0.44420761],
[ 0.55579239, 0.44420761],
[ 0.55579239, 0.44420761],
[ 0.55579239, 0.44420761],
[ 0.55579239, 0.44420761],
[ 0.55579239, 0.44420761],
[ 0.55579239, 0.44420761]])
alpha
是对高要素权重的惩罚,因此alpha
越高,允许权重增长越少,线性模型值变得越不极端,逻辑概率估计越接近到½。通常,使用交叉验证来调整此参数。