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Working with Split Datasets

Long Lynx answered on August 18, 2023 Popularity 7/10 Helpfulness 1/10

answer Working with Split Datasets

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Working with Split Datasets

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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Popularity 7/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: python

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Contributed on Aug 18 2023

Long Lynx

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history = model.fit(X_train_scaled, y_train, epochs=10, batch_size=32, validation_split=0.2)

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Contributed on Aug 18 2023

Long Lynx

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Working with Split Datasets

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from sklearn.metrics import classification_report, roc_auc_score

y_pred = model.predict(X_test_scaled)
y_pred_class = (y_pred > 0.5).astype(int)

print("Classification Report:")
print(classification_report(y_test, y_pred_class))

roc_auc = roc_auc_score(y_test, y_pred)
print(f"ROC AUC: {roc_auc}")

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Contributed on Aug 18 2023

Long Lynx

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Working with Split Datasets

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import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, roc_curve

# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper right')
plt.show()

# Plot training & validation accuracy values
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='lower right')
plt.show()

# Plot confusion matrix
cm = confusion_matrix(y_test, y_pred_class)
print("Confusion Matrix:")
print(cm)

# Plot ROC curve
fpr, tpr, _ = roc_curve(y_test, y_pred)
plt.plot(fpr, tpr, label=f"ROC curve (AUC = {roc_auc:.2f})")
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC)')
plt.legend(loc='lower right')
plt.show()

Popularity 5/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: python

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Contributed on Aug 18 2023

Long Lynx

0 Answers Avg Quality 2/10

Working with Split Datasets

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