Overfitting is a common problem in machine learning where a model learns not only the underlying patterns in the training data but also the noise and outliers. This results in a model that performs exceptionally well on the training dataset but poorly on unseen or test data, because it is too tailored to the training set and lacks the ability to generalize to new data.

In essence, overfitting happens when a model is excessively complex, with too many parameters relative to the amount of training data. A model with high variance will fit the training data perfectly, but small variations in the test data will lead to poor predictions.

To prevent overfitting, several techniques can be employed:

Cross-validation: This involves splitting the dataset into multiple subsets to ensure that the model is trained and validated on different data points. Cross-validation helps assess the model’s ability to generalize.

Pruning (for decision trees): In decision trees, pruning helps to limit the depth of the tree, reducing the complexity of the model and avoiding overfitting by cutting off branches that do not contribute significantly to predictions.

Regularization: Techniques such as L1 (Lasso) and L2 (Ridge) regularization penalize large coefficients in the model, thereby reducing the risk of overfitting by keeping the model simpler.

Early stopping: In iterative models like neural networks, training can be stopped as soon as the model’s performance on a validation dataset starts to degrade, preventing it from overfitting to the training data.

Increasing training data: More data can help the model learn a broader set of patterns, making it more likely to generalize well to new data.

These methods are essential for creating robust models that perform well on both training and unseen data. A data science certification course by The IoT Academy typically covers these strategies, providing you with the tools to build accurate, reliable models.

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