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Trade-off between Minimizing Loss and Minimizing Complexity - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In machine learning, there is often a trade-off between minimizing the loss and minimizing the complexity of the model:
where,
This formulation is commonly associated with regularization techniques in machine learning, particularly in the context of L1 (Lasso) or L2 (Ridge) regularization. The purpose of regularization is to prevent overfitting by penalizing overly complex models. Regularization helps in finding a balance between fitting the training data well and avoiding excessive complexity. The specific form of regularization (L1 or L2) depends on the context and the type of regularization applied. L1 regularization adds the absolute values of the coefficients to the cost function, while L2 regularization adds the squared values. The choice of λ and the type of regularization are typically hyperparameters that need to be tuned during the model training process. The objective is to find the right balance that generalizes well to unseen data. This trade-off is formalized in concepts like regularization, where penalties are applied to the complexity of the model to avoid overfitting. It reflects the balance between fitting the training data and preventing the model from becoming too complex. The trade-off arises because there is often a tension between these two objectives above (minimizing the loss and minimizing the complexity of the model). Improving one aspect may come at the cost of the other. For example:
The range of values for the regularization parameter λ (lambda) depends on the specific implementation and context within machine learning algorithms. The choice of λ is a hyperparameter, and its optimal value is typically determined through a process called hyperparameter tuning:
The actual range of λ values that are explored during hyperparameter tuning depends on the specific algorithm, the dataset, and the goals of the modeling task. Commonly, practitioners use techniques such as cross-validation to evaluate different values of λ and choose the one that results in the best model performance on unseen data. Note that there is no universal range for λ, and the optimal value is problem-dependent. Experimentation and evaluation on validation datasets are crucial for determining the most suitable λ for a given machine learning task. ============================================
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