💡 Measuring neural network success isn't just about accuracy

💡 Measuring neural network success isn't just about accuracy. A key metric to consider is the "Goodness of Fit" (GOF), specifically the "R-squared value" (R2). A high R2 (above 0.85) indicates that the model is effectively capturing the underlying relationships in the data, making it a crucial factor in evaluating model performance.

Think of R2 as a measure of how well the model explains the variability in the data. It ranges from 0 to 1, with higher values indicating better fit. In a well-fitting model, most of the data points lie close to the regression line, indicating a strong relationship between the predicted and actual values.

Here's a breakdown of R2 values:

• 0.00-0.25: Poor fit - the model is not capturing the underlying relationships.
• 0.26-0.50: Fair fit - the model is capturing some, but not most, of the relationships.
• 0.51-0.85: Good fit - the model is effectively capturing most of the relationships.
• 0.86-1.00: Excellent fit - the model is nearly perfectly capt...

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