In the domain of machine learning, recognizing whether a model is underfitting or overfitting
is vital for achieving optimal predictive performance. Underfitting occurs when a model is
overly simplistic, failing to capture the nuanced patterns within the training data, leading to
suboptimal results. On the other hand, overfitting arises when a model becomes excessively
complex, fitting not only the genuine patterns but also the noise in the training data, resulting
in poor generalization to new, unseen data.
To identify underfitting, one must pay close attention to specific signs, notably high training
error and high test error. These red flags suggest that the model struggles to learn from the
training data and is unable to generalize effectively to new, unseen data. In contrast, overfitting
often manifests as a scenario where the training error is significantly lower than the test error,
implying that the model has essentially memorized the training data without truly grasping the
underlying patterns (Javatpoint, 2019).
Addressing these issues requires a multifaceted approach. When dealing with underfitting, one
can explore more complex models with the capacity to capture intricate patterns. This may
involve selecting a different machine learning algorithm or increasing the model's complexity
by adding more layers or units. Additionally, increasing the volume of training data is a
beneficial approach, providing the model with a richer set of examples to learn from. Finetuning hyperparameters such as learning rates and model architecture can further enhance the
model's ability to capture underlying patterns effectively. Feature engineering, the process of
creating more informative features, is another powerful technique that can mitigate underfitting
by providing the model with additional relevant information.
In contrast, addressing overfitting requires techniques aimed at reducing model complexity.
Dropout is a well-known method that involves randomly deactivating some neurons during
training, preventing the model from relying too heavily on any single feature or unit.
Regularization techniques, such as L1 and L2 regularization, add penalty terms to the model's
loss function, discouraging it from fitting noise in the data. Early stopping is an effective
strategy that involves halting the training process when the model's performance on a validation
set starts to deteriorate, preventing it from overfitting (Kasturi, 2021).
Cross-validation serves as a valuable tool for evaluating model performance and fine-tuning
hyperparameters. By dividing the dataset into multiple subsets and training the model on
different combinations of these subsets, you can gain a comprehensive understanding of how
well the model generalizes to unseen data and identify the most suitable hyperparameters for
your specific task.
References:
Javatpoint. (2019). Overfitting and underfitting in machine learning - Javatpoint.
www.javatpoint.com. https://www.javatpoint.com/overfitting-and-underfitting-inmachine-learning
Kasturi, S. N. (2021, December 10). Underfitting and Overfitting in machine learning and
how to deal with it !!! Medium. https://towardsdatascience.com/underfitting-andoverfitting-in-machine-learning-and-how-to-deal-with-it-6fe4a8a49dbf