import numpy
from sklearn import linear_model
# 准备特征数据和标签:
X = numpy.array([3.78, 2.44, 2.09, 0.14, 1.72, 1.65, 4.92, 4.37, 4.96, 4.52, 3.69, 5.88]).reshape(-1,1)
y = numpy.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
# 创建并训练逻辑回归模型:
logr = linear_model.LogisticRegression()
logr.fit(X, y)
# 计算对数几率并转换为几率:
log_odds = logr.coef_ # 获取模型系数(对数几率)
odds = numpy.exp(log_odds) # 通过对数几率计算几率
print(odds)