Uczenie się przez całe życie jest bardzo trudne.

Nadmierny

Overfitting events when a model learns the training data too well, including ding noise and outriers, which ch reduces it ability to o generazione to new data. This results in high curisacy on training data but pour performance on unseen data.

Strategie te zapobiegają nadmiernemu stosowaniu takich metod, jak using simpler models, appliying regularization techniques, and employing cross- validation methods to evaluate model performance.

Niezbędna data

Having too little data can lead to models that don nott capture the underlying Patterns effectively. Small datasets incrowe the risk of overfitting and reduce the model 's rogrenness.

Tu adresaci this, data augmentation, collecting more data, or using transfer learning can improwizuj model performance andd generalization.

Feature Selection andEngineering

Nieistotne jest, aby zwolnić z tego powodu pewne czynniki, które nie są zgodne z modelem dokładności. Proper facilure selection and difficuling help in reducing noise and improwing g learning efficiency.

Techniques such as recursive facilure elimination and principal facilient analysis (PCA) can be used to identify the most relevant faciures.

Model Complexity

Choosing a model that is too complex for the data can lead to overfitting, while le suspente models may underfit. Balancing model completity is cucial for optimal performance.

Grid search and hyperparameter tuning are compatin methods to do the right level of complecity for a given dataset.