What causes model overfitting?

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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.

Model overfitting happens when a machine learning model learns not only the underlying patterns in the training data but also the noise and random fluctuations. As a result, it performs exceptionally well on the training set but poorly on new, unseen data.

Common Causes of Overfitting:

1. Complex Model Architecture:
   Using models that are too complex (e.g., very deep neural networks or decision trees with many branches) relative to the amount of training data can cause the model to memorize the data rather than generalize.

2. Insufficient Training Data:
   When the dataset is too small or not diverse enough, the model may latch onto irrelevant patterns that don’t apply broadly.

3. Too Many Features:
   Having a large number of input features, especially irrelevant or noisy ones, can cause the model to over-specialize on training data.

4. Training for Too Long:
   Excessive training without early stopping allows the model to fit noise.

5. Lack of Regularization:
   Not using techniques like L1/L2 regularization, dropout, or data augmentation can lead to overfitting.

Summary:

Overfitting occurs when a model captures noise instead of the true signal in the training data, reducing its ability to generalize. Preventing overfitting involves balancing model complexity, increasing training data, and using regularization techniques.

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