AI0-001 AI Concepts and Techniques Practice Question
A machine learning engineer is training a logistic regression model and notices that the loss is decreasing very slowly. The learning rate is set to 0.001. What is the MOST likely cause and appropriate fix?
⚠ Common exam trap
A common misconception is that a slow decrease in loss always indicates a learning rate that is too high, when in fact a very low learning rate is the typical cause for slow convergence.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The learning rate is too low; increase it to 0.01
A learning rate of 0.001 is very low for many logistic regression implementations, causing the gradient descent algorithm to take extremely small steps toward the minimum of the loss function. This results in a slow decrease in loss because each weight update is minimal. Increasing the learning rate to 0.01 allows larger steps per iteration, accelerating convergence without typically causing divergence in well-scaled data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The learning rate is too low; increase it to 0.01
Why this is correct
With a learning rate of 0.001, each gradient step barely shifts the weights, so loss falls slowly. Raising it to 0.01 increases the step size, accelerating convergence while remaining stable for logistic regression on typical scaled data.
- ✗
The learning rate is too high; decrease it to 0.0001
Why it's wrong here
A learning rate of 0.001 is already small, so decreasing it to 0.0001 would shrink each parameter update further and slow convergence even more. It is tempting because lowering the rate often stabilises training, but here the rate is the cause of the slow loss decrease, so it should be raised.
- ✗
The model is overfitting; add L2 regularisation
Why it's wrong here
Overfitting shows as a widening gap between training and validation loss, not as uniformly slow loss decrease; L2 regularisation would not accelerate convergence. It is tempting because regularisation is a standard remedy, but it addresses variance, whereas a small learning rate causes the slow progress described.
- ✗
The batch size is too large; reduce it
Why it's wrong here
Batch size affects gradient noise and throughput, not the step magnitude that governs how fast loss falls; reducing it would not fix slow convergence. It is tempting because large batches can slow training, but the stem's explicit learning rate of 0.001 points to step size instead.
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