Explanation:
The correct answers are E. Variance and A. Blending. Variance measures the sensitivity of the function f(X) to changes in the training dataset, which is crucial for avoiding overfitting. Blending, as an ensemble method, can help in reducing variance by combining the predictions of multiple models.
For more details, refer to the Bias-Variance Tradeoff.
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As a Data Scientist working on a deep neural network model with TensorFlow to enhance customer satisfaction for after-sales services, you're tasked with optimizing the model's performance. Your coordinator emphasizes the importance of not only minimizing bias and increasing accuracy during Feature Engineering but also considering another critical factor to avoid potential pitfalls. This factor is crucial for ensuring the model's generalizability across different datasets. Which of the following factors should you also optimize to achieve a balanced model performance? (Choose two correct options)
A
Blending, which refers to an ensemble of ML models.
B
Learning Rate, a hyperparameter in neural networks.
C
Feature Cross, a method for creating new features by multiplying existing ones.
D
Bagging, another ensemble method similar to Blending.
E
Variance, indicating how much the function f(X) can change with a different training dataset.