A loss function, also known as a cost function or objective function, is a mathematical function that quantifies the difference between the predicted values of a machine learning model and the actual values from the training data. It is used to measure the performance of the model during training and to guide the optimization process.
#Types of Loss Functions
- Mean Squared Error
- Mean Absolute Error
- Cross-Entropy Loss
- Binary Cross-Entropy
- Categorical Cross-Entropy
- Hinge Loss
#Importance of Loss Functions
- Model Training: Guides the optimization process by providing a quantitative measure of model performance.
- Hyperparameter Tuning: Helps in selecting the best Hyperparameters by comparing the loss values.
- Model Evaluation: Provides a way to evaluate and compare different models.
#Choosing a Loss Function
- Task Type: Different tasks (regression, classification, etc.) require different loss functions.
- Data Distribution: The choice of loss function can depend on the distribution of the data.
- Model Type: Some models are designed to work better with specific loss functions.
Long Short-Term Memory