Fitting in machine learning refers to the process of training a model to capture patterns in the data, optimizing its parameters to minimize error while ensuring it generalizes effectively to unseen data. This process is often likened to curve fitting, as the model aims to find a mathematical representation of the underlying data relationships. However, unlike traditional curve fitting, machine learning extends to more complex scenarios, including high-dimensional data and non-linear patterns, making it a powerful tool for diverse applications.

Underfitting

Underfitting occurs when a model is too simple to capture the underlying patterns in the data. It leads to high errors on both training and testing datasets, indicating the model has failed to learn the relationships in the data.

Causes of Underfitting:

• Using a model that is too simple (e.g., a linear model for non-linear data).
• Insufficient training (too few epochs or iterations).
• Poor feature selection or inadequate preprocessing.

Solutions:

• Increase model complexity (e.g., use a deeper neural network).
• Train the model for longer or provide more representative features.
• Ensure proper data preprocessing and feature engineering.

Overfitting

Overfitting happens when a model learns the training data too well, including noise and minor variations. It performs exceptionally on training data but poorly on unseen data, failing to generalize.

Causes of Overfitting:

• Excessive model complexity (e.g., too many parameters or layers).
• Training for too long without validation checks.
• Small training dataset that lacks diversity.

Solutions:

• Use regularization techniques (e.g., L1, L2 regularization, or dropout in neural networks).
• Monitor performance with validation data and implement early stopping.