Python 如何将 scikit-learn 的 LogisticRegression 应用于一些十进制数据？
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How do I apply scikit-learn's LogisticRegression for some decimal data?
提问by WoooHaaaa
I've the training data set like this:
我有这样的训练数据集：
0.00479616 | 0.0119904 | 0.00483092 | 0.0120773 | 1
0.51213136 | 0.0113404 | 0.02383092 | -0.012073 | 0
0.10479096 | -0.011704 | -0.0453692 | 0.0350773 | 0
The first 4 columns is features of one sample and the last column is its output.
前 4 列是一个样本的特征，最后一列是其输出。
I use scikit this way :
我这样使用 scikit：
data = np.array(data)
lr = linear_model.LogisticRegression(C=10)
X = data[:,:-1]
Y = data[:,-1]
lr.fit(X, Y)
print lr
# The output is always 1 or 0, not a probability number.
print lr.predict(data[0][:-1])
I thought Logistic Regression always should gives a probability number between 0 and 1.
我认为逻辑回归总是应该给出一个介于 0 和 1 之间的概率数。
采纳答案by Fred Foo
Use the predict_probamethod to get probabilities. predictgives class labels.
使用该predict_proba方法获取概率。predict给出类标签。
>>> lr = LogisticRegression()
>>> X = np.random.randn(3, 4)
>>> y = [1, 0, 0]
>>> lr.fit(X, y)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)
>>> lr.predict_proba(X[0])
array([[ 0.49197272, 0.50802728]])
(If you had read the documentation, you would have found this out.)
（如果您已阅读文档，就会发现这一点。）
