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Gigaversity
Gigaversity.inย โขย 3d
We developed a deep neural network for forecasting internal resource demand across systems, expecting it to outperform traditional models. But in deployment, a simple linear regression delivered better accuracy, stability, and faster inference times. The neural network overfitted to minor seasonal patterns, despite our efforts to reduce this using techniques like regularization. Meanwhile, the linear model focused on the most important features and handled the noise better. The neural network was complex and faced unstable updates during training, but the linear model with simple coefficients produced more consistent predictions. The neural networkโs loss function didnโt account for the costs of incorrect predictions in a business context. The linear model was better aligned with real-world objectives, making it more practical. The linear model avoided unnecessary complexity by selecting the right features, reducing the risk of data leakage. While the neural network struggled with multicollinearity, the linear model maintained clear and simple relationships between features, ensuring easier interpretation. The simpler modelโs predictions aligned better with the business needs, cutting down on operational costs tied to errors. Sometimes, the simplest models outperform the most complex onesโnot because they are technically better, but because they fit the data, context, and business needs more effectively. If you found this relatable or insightful, feel free to like, comment, and share it with your friends who may find this valuable. Follow us on linkedin and stay tuned for the upcoming simulations..
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