How can overfitting be avoided?

Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise, which leads to poor generalization to new, unseen data. To mitigate the risk of overfitting, one can apply various techniques, of which using cross-validation, regularization, and pruning are among the most effective strategies.

Cross-validation involves splitting the dataset into multiple subsets where the model is trained on a portion and validated on another. This helps in assessing how well the model generalizes to unseen data. Regularization adds a penalty to the loss function used during model training, discouraging overly complex models that fit the training data too closely. Pruning, especially in decision trees or complex models, involves removing parts of the model that do not improve its performance on validation data, thus simplifying it.

While larger datasets can help reduce the chances of overfitting by providing more examples for the model to learn from, it is not a guaranteed solution, particularly if the model itself is too complex. Therefore, exclusively using larger datasets may not be sufficient on its own to tackle the overfitting problem. Ignoring the complexity of the model can lead to either underfitting, where the model is too simple, or still result in overfitting