Limitations and Areas for Improvement
Enhancing Similarity Calculation The current similarity calculation formula requires refinement to better capture the unique characteristics of each project and reflect the impact of traits accurately. In particular, in-game NFTs often exhibit complex interactions between traits, influencing their overall value. Updating the similarity calculation method will enable a more nuanced evaluation of NFT prices, considering how specific traits complement or limit each other.
Comprehensive Price Determinants While our AI-driven model considers parameters like rarityScore and utilityScore, it is crucial to acknowledge that NFT prices are influenced by a myriad of factors beyond these parameters. Market trends in the blockchain space, overall project performance, social signals, and historical ownership data all play pivotal roles in determining the price of an NFT. Integrating these additional factors into our model will yield a more comprehensive and accurate pricing mechanism.
Ensemble Modeling Approach To enhance predictive accuracy, we recognize the potential benefits of adopting an ensemble modeling approach. Currently, the models operate individually, but combining their outputs could lead to more robust and reliable price predictions. Implementing ensemble methods, such as model averaging or stacking, may result in superior performance compared to using individual models in isolation.
Continuous Data Reviews As the NFT market is highly dynamic and constantly evolving, the effectiveness of our model requires continuous evaluation and adaptation. We plan to regularly update the review section with actual data from new transactions in the future. This iterative process will help us refine our model and ensure it remains responsive to emerging market trends and changing dynamics.
Interpretability and Explainability Understanding the factors that influence the output results of our model is essential for users' trust and confidence. To achieve this, we are committed to developing an interpretable and explainable AI model. Techniques such as feature importance analysis and model visualization will allow us to elucidate how specific factors affect the predicted NFT prices. By providing transparent explanations, users can make more informed decisions based on the model's output.
Conclusion:
As we progress in the development of the AI-driven NFT price evaluation tool, we are dedicated to overcoming the identified limitations and implementing necessary improvements. Addressing the challenges of similarity calculation, incorporating comprehensive price determinants, exploring ensemble modeling, and regularly updating data reviews will fortify the robustness and relevance of our model.
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