AutoML Results Interpreter AI Prompt

I ran an AutoML tool on my dataset and got a results report. Help me understand what it means in plain English.

AutoML output: {{automl_output}}

1. What model was selected and why:
   - What is the winning model type? (e.g. gradient boosting, random forest, neural network)
   - Explain what this type of model does in one sentence without jargon
   - Why did it win? What does it do that the other models did not?

2. How good is the model — in plain terms:
   - What does the accuracy metric mean? Translate it to business impact:
     - If accuracy is 85%, that means the model is wrong about 1 in 7 predictions
     - If AUC is 0.82, that means the model ranks a randomly chosen positive case above a randomly chosen negative case 82% of the time
   - Is this result good, okay, or poor? Give me context: what would random guessing score?
   - What is the most common type of mistake the model makes?

3. What does the model think matters most:
   - Which features (columns) did the model find most useful for making predictions?
   - Do these make intuitive sense? If a feature that should not matter ranks highly, that could indicate a data problem.
   - Is there any feature you are surprised is not on the list?

4. Should I trust this model:
   - Is there any sign of overfitting? (training accuracy much higher than validation accuracy)
   - Was the dataset large enough? As a rough guide: at least 1000 rows for simple problems, 10,000+ for complex ones
   - Are there any warnings in the AutoML report I should pay attention to?

5. Next step:
   - Based on this report, what is the one thing I should do next? (deploy it, get more data, investigate a specific feature, try a different approach)