Regularization §

Group: 4 #group-4

Relations §

• Regularization Path: The regularization path shows how the coefficients of a regularized model change as the regularization strength is varied.
• Elastic Net: Elastic Net is a combination of Ridge and Lasso regression, using both L1 and L2 penalty terms.
• Neural Networks: Regularization techniques, such as L1/L2 regularization or dropout, are used to prevent overfitting in neural networks.
• Early Stopping: Early stopping is a form of regularization that stops the training process when the validation error starts to increase, preventing overfitting.
• Lasso Regression: Lasso regression is a regularization technique that adds a penalty term equal to the absolute value of the magnitude of the coefficients to the cost function, leading to sparse models.
• Model Complexity: Regularization controls the complexity of the model by adding a penalty term to the loss function.
• Dropout: Dropout is a regularization technique used in neural networks, where randomly selected neurons are ignored during training.
• Underfitting: Too much regularization can lead to underfitting, where the model is too simple and fails to capture the underlying patterns in the data.
• Generalization: Regularization improves the generalization ability of a model by preventing overfitting to the training data.
• Bias-Variance Tradeoff: Regularization techniques, such as L1 or L2 regularization, can be used to control the complexity of the model and prevent overfitting, helping to find a balance between bias and variance.
• Underfitting: Regularization techniques, such as L1 or L2 regularization, can help prevent underfitting by introducing a penalty for model complexity.
• Deep Learning: Regularization techniques like dropout and L1/L2 regularization help prevent overfitting in deep learning models.
• Convolutional Neural Networks: Regularization techniques like dropout are used to prevent overfitting in Convolutional Neural Networks
• Ridge Regression: Ridge regression is a type of regularization that adds the L2 penalty term to the loss function.
• Lasso Regression: Lasso regression is a type of regularization that adds the L1 penalty term to the loss function.
• L1 Regularization: L1 regularization, also known as Lasso regression, adds the sum of absolute values of the coefficients to the loss function as a penalty term.
• Backpropagation: Regularization techniques, such as L1 and L2 regularization, can be used in conjunction with backpropagation to prevent overfitting.
• Regression: Regularization techniques, such as ridge regression and lasso regression, are used to prevent overfitting in regression models by adding a penalty term to the cost function.
• Bias-Variance Tradeoff: Regularization helps balance the bias-variance tradeoff by reducing the variance (overfitting) at the cost of increasing the bias (underfitting).
• L2 Regularization: L2 regularization, also known as Ridge regression, adds the sum of squared values of the coefficients to the loss function as a penalty term.
• Ridge Regression: Ridge regression is a regularization technique that adds a penalty term equal to the square of the magnitude of the coefficients to the cost function.
• Hyperparameters: The regularization strength is often controlled by a hyperparameter, which needs to be tuned using techniques like cross-validation.
• Cross-Validation: Cross-validation is used to select the optimal regularization strength by evaluating the model’s performance on held-out data.
• Overfitting: Regularization helps prevent overfitting by adding a penalty term to the loss function, which limits the complexity of the model.