关于autokera预测标签概率的方法

关于autokera这个自动机器学习算法，进行二分类预测时，除了预测标签，还可以预测每个标签的概率吗？就如同别的机器学习的
predict_proba。下面是我的代码

import pandas as pd
import tensorflow as tf
import autokeras as ak
TRAIN_CSV_DIR="train.csv"
TEST_CSV_DIR="test.csv"

# Initialize the structured data classifier.
clf = ak.StructuredDataClassifier(
    overwrite=True, max_trials=3
)  # It tries 3 different models.
# Feed the structured data classifier with training data.
clf.fit(
    # The path to the train.csv file.
    TRAIN_CSV_DIR,
    # The name of the label column.
    "isDefault",
    epochs=10,
)
# Predict with the best model.
predicted_y = clf.predict(TEST_CSV_DIR)
# Evaluate the best model with testing data.
print(clf.evaluate(TEST_CSV_DIR, "isDefault"))

“Devil组”引证GPT后的撰写：
可以在predict()函数中添加参数output_probabilities=True来得到每个标签的概率。修改你的代码如下：

import pandas as pd
import tensorflow as tf
import autokeras as ak
TRAIN_CSV_DIR="train.csv"
TEST_CSV_DIR="test.csv"
Initialize the structured data classifier.

clf = ak.StructuredDataClassifier(
overwrite=True, max_trials=3
) # It tries 3 different models.
Feed the structured data classifier with training data.

clf.fit(
# The path to the train.csv file.
TRAIN_CSV_DIR,
# The name of the label column.
"isDefault",
epochs=10,
)
Predict with the best model and output probabilities.

predicted_y, predicted_prob = clf.predict(TEST_CSV_DIR, output_probabilities=True)
Evaluate the best model with testing data.

print(clf.evaluate(TEST_CSV_DIR, "isDefault"))