Complexité du modèle is a term in apprentissage automatique that describes how complex a model is in terms of its structure and capacity to learn from data. It involves various factors, including the number of parameters, the depth of réseaux neuronaux, and the architecture globale du modèle.
In general, more complex models have a greater capacity to fit intricate patterns in data, which can lead to better performance on training datasets. However, this increased complexity also raises the risk of overfitting, where the model learns noise and specific details from the données d'entraînement rather than generalizable patterns. This can result in poor performance on unseen data, highlighting a critical trade-off between bias and variance.
La complexité du modèle peut être contrôlée par des techniques telles que regularization, which penalizes overly complex models, and la sélection de modèles, which involves choosing the simplest model that adequately captures the data structure.
Ultimately, finding the right level of model complexity is essential for effective machine learning, as it directly influences the model’s ability to generalize well to new, unseen datasets.