我正在尝试在高斯NB机器学习模型上运行二进制日志丢失。据我了解，我需要使用.predict_proba函数确定每次预测的概率。
完整代码：
import pandas as pd
import numpy as np
# load csv files
testdf = pd.read_csv("train_predictions.csv")
testlabelsdf = pd.read_csv("train_labels.csv")
predictdf = pd.read_csv("export_dataframe.csv")
#change output settings
pd.set_option("display.width", 400)
pd.set_option("display.max_columns", 20)
pd.set_option("display.max_rows", 200)
# transform str to int
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
testdf["ID"] = le.fit_transform(testdf["ID"])
predictdf["ID"] = le.fit_transform(predictdf["ID"])
testdf["THAL"] = le.fit_transform(testdf["THAL"])
predictdf["THAL"] = le.fit_transform(predictdf["THAL"])
# ID is not relevant to model, HEART DZ will be our target
# train dataset
cols = [col for col in testdf.columns if col not in ["ID"]]
data = testdf[cols]
target = testlabelsdf["HEART DZ"]
# predict dataset
colspred = [col for col in predictdf.columns if col not in ["ID"]]
predictdf = predictdf[colspred]
from sklearn.model_selection import train_test_split
# split dataset
data_train, data_test, target_train, target_test = train_test_split(data, target, random_state=10) #test_size=0.30,random_state=10)
# Gaussian Naive Bayes
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
gnb = GaussianNB()
gnb.fit(data_train, target_train)
target_pred = gnb.predict(data_test)
ac = accuracy_score(target_test, target_pred, normalize=True)
# apply .predict function to predict dataset
yNew = gnb.predict(predictdf)
prob = target_pred.predict_proba(yNew) # here is where the error is originating
print(prob)
错误：
AttributeError: 'numpy.ndarray' object has no attribute 'predict_proba'