What is overfitting, and how can it be prevented?

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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 is a common problem in machine learning where a model learns the training data too well, including its noise and outliers, resulting in excellent performance on the training set but poor generalization to new, unseen data.

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🔹 What is Overfitting?

• The model becomes overly complex and captures random fluctuations or noise instead of the underlying pattern.

• It performs well on training data but poorly on validation/test data.

• It indicates low bias but very high variance.

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🔹 How to Prevent Overfitting?

1. Use More Training Data
   Increasing the size of the training dataset helps the model learn the true patterns better.

2. Simplify the Model
   Use simpler models with fewer parameters or reduce the number of features (feature selection).

3. Regularization
   Techniques like L1 (Lasso) and L2 (Ridge) add a penalty to large coefficients, discouraging complexity.

4. Cross-Validation
   Use techniques like k-fold cross-validation to ensure the model generalizes well.

5. Early Stopping
   Stop training when performance on validation data starts degrading.

6. Data Augmentation
   In domains like image processing, augment data by transformations to increase diversity.

7. Dropout (for Neural Networks)
   Randomly "drop" neurons during training to prevent reliance on specific pathways.

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