Overfitting and underfitting in machine learning

In machine learning, models are trained on training data to make accurate predictions on test data. The ideal model demonstrates strong performance on test data, indicating effective generalization. However, models can sometimes display differing performance patterns: they might excel on training data but perform poorly on test data, or they might underperform on both. These issues are referred to as overfitting and underfitting, respectively. This article examines these challenges, explaining their causes and outlining strategies to address them.

Overfitting occurs when the model performs well on the training data but the performance decreases on the test data. An overfit model fails to generalize as it learns the noise and random fluctuations from data in addition to patterns. 

1. This excessive learning causes the model to be overly complex, making it sensitive to minor variations in the training data and resulting in poor performance on unseen data.

An overfit model fails to generalize and the reasons for overfitting are given below:

• Complex models with too many parameters: models with too many parameters are complex and perform well on training data but do not generalize well.
• Insufficient training data: Models trained on limited data tend to memorize it instead of the generalized patterns in data.
• Noise in data: the irrelevant information and noise in data causes the model not to generalize well.
• Training for a long time: Overfitting also occurs when the model is trained on data for a longer time.

To detect the overfitting following techniques can be used:

• Traine validation split: The dataset is split into train and validation sets. The performance of the model is monitored on the validation set. A significant performance gap between the training and validation/test sets is a sign of overfitting.
• Cross-validation technique: k-fold cross-validation can be used to detect overfitting by monitoring the performance of the model on train and test data during training.

Overfitting can be reduced by scaling and removing noise from the data. In addition to this following techniques can be used:

• Simplifying the model: reducing the model parameters reduces the complexity of the model which can prevent overfitting. Using the simpler model also reduces the overfiting.
• Early stopping: early stopping helps to prevent overfitting by stopping the model training at the point where the loss starts to increase.
• Regularization: Regularization techniques add a penalty for larger coefficients, discouraging the model from fitting the noise.
• K-fold cross-validation: Using cross-validation ensures the model’s performance is tested on different subsets of the data, helping to detect and prevent overfitting.
• Pruning in decision tree: Pruning removes parts of the tree that are not critical for making predictions, reducing overfitting.

The model is said to underfit when the performance of the model is low on both the train and test sets. The model does not perform well on trai data and is also not able to generalize well.

1. The model is too simple to capture the hidden patterns in data.
2. Evaluation measure values are low train and unseen data.

Several factors lead to underfiting.

• Simple model: Using the simple model is the cause of underfiting. Such a model is not able to capture the complexities in data. For example, using the linear model for data having non-linear relationships will result in underfitting.
• Poor feature selection: A model is underfitting if it is trained on an inadequate number of features that do not capture enough information to make accurate predictions.
• Use of more regularization: To prevent overfitting regularization techniques are used but the use of excessive regularization causes the model to underfit.
• Insufficient training time: In iterative algorithms such as neural networks insufficient training can prevent the model from learning the underlying patterns.

The following techniques can be used to prevent underfitting.

• Using the more complex model: Increasing the model complexity prevents underfitting. This might involve using polynomial regression instead of linear regression or using a more complex model like random forest and neural network.
• Feature engineering: Increasing the quality and quantity of features reduces the underfitting. Feature engineering is used for this purpose that create new features adding more information to data.
• Optimize hyperparameters: To prevent underfitting the model hyperparameters are adjusted to suit the data. This involves reducing the regularization.