为什么我的 accuracy_score 指标不正确？ scikit学习

Question

我有有点的工作代码，这给我带来了麻烦。我似乎得到了一个几乎随机的 accuracy_score 指标，而我的预测值打印输出表明并非如此。我一直在关注 this 在线教程，这是我目前所写的内容：

import os
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, confusion_matrix

adult_train = pd.read_csv(os.path.expanduser("~/Desktop/") + "adult_train_srt.csv", sep=',')
print(adult_train.head(100))

le = LabelEncoder()
adult_train['age'] = le.fit_transform(adult_train['age'])
adult_train['workclass'] = le.fit_transform(adult_train['workclass'].astype(str))
adult_train['education'] = le.fit_transform(adult_train['education'].astype(str))
adult_train['occupation'] = le.fit_transform(adult_train['occupation'].astype(str))
adult_train['race'] = le.fit_transform(adult_train['race'].astype(str))
adult_train['sex'] = le.fit_transform(adult_train['sex'].astype(str))
adult_train['hours_per_week'] = le.fit_transform(adult_train['hours_per_week'])
adult_train['native_country'] = le.fit_transform(adult_train['native_country'].astype(str))
adult_train['classs'] = le.fit_transform(adult_train['classs'].astype(str))

cols = [col for col in adult_train.columns if col not in ['classs']]
data = adult_train[cols]
target = adult_train['classs']

data_train, data_test, target_train, target_test = train_test_split(data, target, test_size = 0.1) #, random_state = 42)

gnb = GaussianNB()
pred = gnb.fit(data_train, target_train).predict(data_test)
pred_gnb = gnb.predict(data_test)
print(pred_gnb)

print("Naive-Bayes accuracy: (TN + TP / ALL) ", accuracy_score(pred_gnb, target_test)) #normalize = True
print("""Confusion matrix:
TN - FP
FN - TP
Guessed:
0s +, 1s -
0s -, 1s +
""")
print(confusion_matrix(target_test, pred_gnb))

Prediction = pd.DataFrame({'Prediction':pred_gnb})

result = pd.concat([adult_train, Prediction], axis=1)
print(result.head(10))

我很茫然，我无法理解我的数据帧连接是否有效，或者 accuracy_score 指标是否解决了其他问题，因为我得到的输出如下：

这个特定的实例表示有 7 个真阴性 (OK)、1 个假阳性 (???)、2 个假阴性 (O.K) 和 0 个真阳性 (???, 但是有一个猜对了吗？）。 [classs] 列是 [Prediction] 列猜测的内容。

Answer 1

result = pd.concat([adult_train, Prediction], axis=1)

此处为预测数据帧，不应与 adult_train 连接，预测是在测试集上预测的结果data_set

pred_gnb = gnb.predict(data_test)

所以，我认为你应该连接 data_test、target_test 和预测，试试这个，它可能会起作用

result = pd.concat([pd.DataFrame(data_test), pd.DataFrame(target_test), Prediction], axis=1)

为什么我的 accuracy_score 指标不正确？ scikit学习

Why is my accuracy_score metric incorrect? scikit learn

python

machine-learning

scikit-learn

naivebayes