Detect and Prevent Overfitting and Underfitting in Machine Learning

Machine learning models’ ability to produce accurate predictions is called as generalization. However, overfitting and underfitting are two common issues that can severely degrade the performance of machine learning models. Those machine learning issues can lead to several significant problems:

• Wasted resources
• Misinformed decision-making
• Lack of trust
• Data collection bias
• Regulatory and compliance issues

Generalization is the model’s ability to make accurate predictions. However, if your model doesn’t generalize properly, it can cause problems for the organization. But, what is overfitting and underfitting in machine learning anyway?

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Overfitting: Defined

Overfitting is a common problem in machine learning where a model tries to cover all the data points present in the dataset. The model begins to catch noise and inaccurate values which lower its efficiency and accuracy. In other words, a model performs poorly on new data because it has essentially memorized the training data rather than generalized from it.

Key characteristics of overfitting include:

• Low Training Error, High Test Error: Overfit models have low error on the training data as they have fit the noise in it. However, when presented with new data, they struggle to generalize from the training data.
• High Variance: Overfit models have high variance, meaning they are sensitive to small changes in the training data. This sensitivity can lead to widely varying predictions.

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Usually, overfitting happens because of the following causes:

• Complex Models: Models that are excessively complex can capture noise in the training data, leading to poor generalization.
• Noise in Data: Noise in the data can mislead the model into capturing random variations rather than true patterns.
• Outliers: Outliers, which are extreme data points that don’t represent the typical behavior of the data, can disproportionately influence the model’s learning process.

Underfitting: Defined

Underfitting in machine learning occurs when a model is too simple or lacks the capacity to capture the underlying patterns in the training data. Underfitting typically happens when the model is not complex enough to represent the true relationships within the data, failing them to find the dominant trend.

Key characteristics of underfitting include:

• Simplified Model: Underfit models are often too simplistic for the complexity of the data, making them incapable of modeling the true relationships.
• Bias: Underfitting is often associated with high bias, where the model makes strong assumptions about the data that don’t hold true, leading to inadequate model performance.

Usually, overfitting happens because of the following causes:

• Simplistic Models: The dataset used is too simple. The model may be incapable of representing the underlying patterns in the data.
• Overly Strict Regularization: Used excessively, regularization might muzzle the model, inhibiting its ability for learning.
• Insufficient Features: If the model isn’t given enough features or if the features don’t have enough data to make predictions, it doesn’t have enough information to make accurate predictions.

Detect and Prevent Overfitting & Underfitting

When overfitting occurs, your model learns ‘too much’ from the data, but when underfitting occurs, your model ‘isn’t learning’ enough. Thus, it’s crucial to know how to detect and prevent them so that your predictions remain accurate.

Detecting Overfitting

• Holdout Validation: Comparing a model to a holdout set to check if it too closely resembles the training data.
• Cross-Validation: Splitting the data into K folds, training the model K times on different combinations of these folds, and evaluating its performance.
• Learning Curves: Plot the model’s training and validation performance. The training error will decrease while the validation error increases or plateaus, indicating overfitting.

Detecting Underfitting

• Performance Metrics: Monitor performance metrics on the training and validation datasets. If it performs poorly, it indicates underfitting.
• Visual Inspection: Visualize the model’s predictions, and if it consistently fails to match the actual data, it may suggest underfitting.

Model Complexity: If your model is too simple, it has less capacity to learn the relationships in the data.

How to Prevent Overfitting & Underfitting

1. Use appropriate data: The quality and quantity of the data you use to train your model is crucial to avoid missing, noisy, or imbalanced data.
2. Apply regularization: It decreases the complexity of a model and prevents overfitting by including a penalty term in the loss function.
3. Use early stopping: It stops the training process as soon as the model’s performance declines to avoid learning too much and losing its capacity to generalize.
4. Use data augmentation: It improves the size and diversity of the data by applying adjustments to the original data, giving the model more scenarios to learn from while also preventing overfitting.