What is overfitting in machine learning?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They are designed to recognize patterns and relationships in data through a process of learning.

Overfitting in machine learning refers to a model that learns not only the underlying patterns in the training data but also the noise and random fluctuations. As a result, the model performs very well on the training data but poorly on new, unseen data (i.e., it has poor generalization).

Signs of Overfitting:

• High accuracy on training data.

• Low accuracy on validation/test data.

• Model performs inconsistently across different datasets.

Causes of Overfitting:

• Model is too complex (too many parameters or layers).

• Training data is too small or noisy.

• Training for too many epochs.

• Lack of regularization.

Example:

Imagine you're trying to fit a curve to a set of data points. A simple linear regression might underfit, missing the true pattern. A very complex polynomial might perfectly pass through every training point, but it will likely fail on new data — that’s overfitting.

How to Prevent Overfitting:

• Use simpler models.

• Apply regularization techniques (like L1 or L2 penalties).

• Use cross-validation.

• Prune decision trees or dropout in neural networks.

• Get more training data.

• Implement early stopping during training.

Let me know if you want a visual example or a code demonstration!

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