sklearn in predict_proba usage (the difference between the note and predict)

     predict_proba returns an array of n rows and k columns,  the j-th column of the i-th row value is the i-th prediction model to predict the probability of a label for the sample, and the probability for each row is 1.

# conding :utf-8
from sklearn.linear_model import LogisticRegression
import numpy as np
x_train = np.array([[1,2,3],
                    [1,3,4],
                    [2,1,2],
                    [4,5,6],
                    [3,5,3],
                    [1,7,2]])
 
y_train = np.array([3, 3, 3, 2, 2, 2])
 
x_test = np.array([[2,2,2],
                   [3,2,6],
                   [1,7,4]])
 
clf = LogisticRegression()
clf.fit(x_train, y_train)
 
# 返回预测标签
print(clf.predict(x_test))
 
# 返回预测属于某标签的概率
print(clf.predict_proba(x_test))
 
# [2 3 2]
# [[0.56651809 0.43348191]
#  [0.15598162 0.84401838]
#  [0.86852502 0.13147498]]
# 分析结果：
# 预测[2,2,2]的标签是2的概率为0.56651809，3的概率为0.43348191
#
# 预测[3,2,6]的标签是2的概率为0.15598162，3的概率为0.84401838
#
# 预测[1,7,4]的标签是2的概率为0.86852502，3的概率为0.13147498