How can I diagnose issues with my Akita AI model?
To diagnose issues with your Akita AI model, start by checking the training data for quality and relevance.
What specific metrics should I look at?
Focus on accuracy, precision, recall, and F1 score to evaluate your model's performance.
How can I improve the model if the metrics are low?
Consider augmenting your training data, tuning hyperparameters, or trying different algorithms.
Are there any tools that can help with this process?
Yes, tools like TensorBoard for visualization and Scikit-learn for model evaluation can be very helpful.
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