评估模型给出的精度不等于sklearn分类_报告精度 [英] Accuracy given from evaluating model not equal to sklearn classification_report accuracy

问题描述

我正在使用sklearn category_report报告测试统计信息.该方法给出的准确度为42％，而模型评估给出的准确度为93％.真正的准确度是哪一个?造成这种差异的原因是什么?

I'm using sklearn classification_report for reporting test statistics. The accuracy given by this method is 42% while model evaluation gives 93% accuracy. Which one is the real accuracy and what's the reason of this difference?

模型评估:

results = model.evaluate(test_ds.values, test_lb.values) print(results)

输出:
7397/7397 [==============================] - 0s 28us/sample - loss: 0.2309 - acc: 0.9305

报告分类:

import numpy as np from sklearn.metrics import classification_report predictions = model.predict(test_ds) print(classification_report(test_lb, np.argmax(predictions, axis=1)))

输出:

Model evaluation:

results = model.evaluate(test_ds.values, test_lb.values) print(results)

Output:
7397/7397 [==============================] - 0s 28us/sample - loss: 0.2309 - acc: 0.9305

Report Classification:

import numpy as np from sklearn.metrics import classification_report predictions = model.predict(test_ds) print(classification_report(test_lb, np.argmax(predictions, axis=1)))

Output:

label precision recall f1-score support

       0       0.41      0.38      0.40      3700
       1       0.43      0.46      0.44      3697

accuracy                           0.42      7397

推荐答案

理想情况下，两个度量都应给出相同级别的准确性，但有一些细微差别.问题可能出在数据上.

Ideally, both the metrics should give the accuracy of the same level with some minor difference. The problem may be with the data.

您可以看到以下示例，以比较两个指标.

You can see the below example to compare both the Metrics.

import tensorflow as tf
from sklearn.datasets import load_iris
import numpy as np
from tensorflow import keras
from sklearn.model_selection import train_test_split
iris = load_iris()
X = iris.data[:, (2, 3)] # petal length, petal width
y = (iris.target == 0).astype(np.int) 

(X_train,X_test,y_train,y_test) = train_test_split(X,y,test_size=0.2)

model = keras.models.Sequential([
    keras.layers.Flatten(input_shape=[2]),
    keras.layers.Dense(300, kernel_initializer="he_normal"),
    keras.layers.LeakyReLU(),
    keras.layers.Dense(100, kernel_initializer="he_normal"),
    keras.layers.LeakyReLU(),
    keras.layers.Dense(1, activation="sigmoid")
])

model.compile(loss="binary_crossentropy",
              optimizer=keras.optimizers.SGD(),
              metrics=["accuracy"])

model.fit(X_train,y_train,epochs=2)

培训准确性:

Epoch 1/2
4/4 [==============================] - 0s 3ms/step - loss: 2.0655 - accuracy: 0.6333
Epoch 2/2
4/4 [==============================] - 0s 3ms/step - loss: 0.5199 - accuracy: 0.7333
<tensorflow.python.keras.callbacks.History at 0x7fdd4ed72048>  

评估结果:

test_ds = pd.DataFrame(X_test)
test_lb = pd.DataFrame(y_test)
model.evaluate(test_ds.values,test_lb.values)

1/1 [==============================] - 0s 1ms/step - loss: 0.5510 - accuracy: 0.6667
[0.5510352253913879, 0.6666666865348816] 

使用Sklearn指标:

import numpy as np
from sklearn.metrics import classification_report
predictions = model.predict(X_test)
print(classification_report(y_test, np.argmax(predictions, axis=1))) 

              precision    recall  f1-score   support

           0       0.67      1.00      0.80        20
           1       0.00      0.00      0.00        10

    accuracy                           0.67        30
   macro avg       0.33      0.50      0.40        30
weighted avg       0.44      0.67      0.53        30 

您可以看到两个指标的准确性都相同(66.7和67).

You can see the accuracy is the same for both metrics(66.7 & 67).

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