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Answer: Decrease the learning rate of the model.
## Explanation **C is correct.** The described issue where the loss function converges to different stable values during multiple runs with identical parameters suggests the neural network is finding different local minima rather than consistently reaching the global minimum. This is a common problem in neural network optimization. **Key Points:** - The **learning rate** controls the step size in gradient descent optimization - If the learning rate is too large, the optimization can oscillate between different sides of the optimization landscape - This oscillation can cause the algorithm to settle in different local minima on different runs - **Decreasing the learning rate** makes smaller, more precise steps, helping the algorithm find the global minimum more consistently **Why other options are incorrect:** - **A:** Confusion matrices are for model evaluation (classification performance), not for addressing optimization convergence issues - **B:** Adaptive boosting (AdaBoost) is an ensemble method for improving classification accuracy, not for solving gradient descent convergence problems - **D:** Increasing the learning rate would worsen the oscillation problem, making it more likely to overshoot minima and find different local solutions The team should decrease the learning rate to achieve more stable convergence to the global minimum of the loss function.
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A team of data scientists at a global asset management firm is working on a neural network (NN) model to assess how the choice of metrics used by the firm to select equity investments relates to stock performance. In particular, the team designs the NN model to find the combination of weights for these metrics that generates the highest risk-adjusted return in the firm's portfolios. The team optimizes the model during the training process by varying multiple parameters and observes that, during multiple runs with identical parameters, the loss function converges to different, yet still stable, values. Which of the following is the most appropriate action for the team to take to address this issue?
A
Construct a confusion matrix to train the model.
B
Apply adaptive boosting when training the model.
C
Decrease the learning rate of the model.
D
Increase the learning rate of the model.