Accuracy given from evaluating model not equal to sklearn classification_report accuracy

Question

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

Answer 1

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

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)

Training Accuracy:

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>  

Evaluation Result:

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] 

With Sklearn Metrics:

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 

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