Related to Cost Function
A Loss function guides model training by providing a measure of prediction accuracy. The ultimate aim is to minimize the loss, ensuring the model makes accurate predictions on unseen data.
This function measures how well a model’s predictions match the actual target values. It quantifies the error between the predicted output and the true output. Examples include Mean Squared Error (MSE) for regression and Cross-Entropy for classification. The loss function is what the model aims to minimize during training.
Terminology
- Loss Function: Also referred to as a cost function, error function, or objective function. It is a component in training machine learning models.
Description
-
Purpose: A loss function quantifies how well a model’s predictions align with the actual target values. It serves as a guide for the optimization process during model training.
-
Minimization: The primary goal is to minimize the loss function, which indicates that the model’s predictions are becoming more accurate.
Evaluation
-
Disparity Measurement: The loss function measures the difference between predicted and actual values, providing a metric for model evaluation.
-
Training and Evaluation: Loss functions are used both during training (to adjust model parameters) and evaluation (to assess model performance).
Resources
Loss Function This function measures how well a model’s predictions match the actual target values. It quantifies the error between the predicted output and the true output. Examples include Mean Squared Error (MSE) for regression and Cross-Entropy for classification. The loss function is what the model aims to minimize during training.
Loss Function This function measures how well a model’s predictions match the actual target values. It quantifies the error between the predicted output and the true output. Examples include Mean Squared Error (MSE) for regression and Cross-Entropy for classification. The loss function is what the model aims to minimize during training.
Loss Function This function measures how well a model’s predictions match the actual target values. It quantifies the error between the predicted output and the true output. Examples include Mean Squared Error (MSE) for regression and Cross-Entropy for classification. The loss function is what the model aims to minimize during training.
Loss Function This function measures how well a model’s predictions match the actual target values. It quantifies the error between the predicted output and the true output. Examples include Mean Squared Error (MSE) for regression and Cross-Entropy for classification. The loss function is what the model aims to minimize during training.
Loss Function This function measures how well a model’s predictions match the actual target values. It quantifies the error between the predicted output and the true output. Examples include Mean Squared Error (MSE) for regression and Cross-Entropy for classification. The loss function is what the model aims to minimize during training.