How does overfitting impact machine learning model performance?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in  The primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem. 

Overfitting happens when a machine learning model learns the training data too well, including noise, errors, and random fluctuations, instead of just the underlying patterns. This makes the model perform extremely well on training data but poorly on new, unseen data.

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🔎 Impact of Overfitting on Model Performance

1. Poor Generalization

   • The model memorizes training examples instead of learning general patterns.

   • As a result, it struggles to make accurate predictions on test data or in real-world scenarios.

2. High Training Accuracy, Low Test Accuracy

   • Overfit models often show near-perfect accuracy on training sets.

   • But when applied to unseen data, accuracy drops significantly.

3. Increased Complexity

   • The model may become unnecessarily complex, using too many features or parameters.

   • Complex models are harder to interpret and maintain.

4. Wasted Resources

   • Training an overly complex model consumes more computation and memory.

   • Despite the cost, it delivers poor real-world results.

5. False Sense of Reliability

   • Stakeholders may believe the model is highly accurate due to training results.

   • But in production, its predictions may mislead decisions.

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✅ In short: Overfitting reduces a model’s ability to generalize to new data, leading to poor real-world performance despite excellent training results.

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