overfitting

the model suffered from overfitting and performed poorly on new data.

we need to avoid overfitting during the training process.

regularization techniques can help prevent overfitting in machine learning.

overfitting occurs when a model learns the training data too well.

cross-validation is a common method to detect overfitting.

the risk of overfitting is higher with complex models.

we used dropout layers to mitigate overfitting in the neural network.

careful feature selection can reduce the likelihood of overfitting.

the validation set helps us identify and address overfitting issues.

early stopping is a strategy to prevent overfitting on the training data.

we evaluated the model's performance to check for overfitting.