Understanding Overcontrol in Machine Learning
Overcontrolled refers to a situation where the model is too precise and captures the noise in the data, resulting in poor generalization performance. In other words, the model is overfitting to the training data, and it does not generalize well to new, unseen data.
In an overcontrolled model, the coefficients of the features are too large, and the model is able to fit the noise in the data exactly, but this precision comes at the cost of poor generalization performance. The model becomes too specialized to the training data and fails to capture the underlying patterns in the data.
To avoid overcontrol, it is important to use appropriate regularization techniques, such as L1 or L2 regularization, to penalize large coefficients and prevent overfitting. Additionally, techniques such as cross-validation can be used to evaluate the model's performance on new data and prevent overfitting.
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