In the last class I have learnt about
Overfitting: When a model learns the training data too well but struggles to generalize to new data due to its excessive complexity.
Underfitting: When a model is too simple to capture the patterns in the training data and performs poorly both on the training and new data.
to train any data In our overfitting and underfitting guide the selection of appropriate model complexity. Data scientists use techniques like cross-validation and regularization to prevent overfitting (excessive model complexity) or underfitting (overly simplistic models). Hyperparameter tuning is essential to fine-tune models and strike the right balance between these extremes. Regular model evaluation and maintenance ensure that the chosen model continues to generalize well and perform effectively on new data throughout the project.
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