The accuracy of predictive models depends on several factors:
Data Quality: High-quality, comprehensive datasets lead to more accurate predictions. Model Complexity: More sophisticated models can capture complex behaviors but may require more computational power. Algorithm Selection: The choice of algorithm impacts the model's ability to generalize from training data to new scenarios.
In many cases, predictive models can achieve high accuracy, but they are often validated and refined through experimental data.
