The cost function in machine learning depends on the predicted output of the model and the actual target values. It measures how well the model performs by quantifying the difference between these two values.
Here's a breakdown:
- Predicted Output: This is the output generated by the machine learning model based on the input data.
- Actual Target Values: These are the true or desired values that the model is trying to predict.
The cost function essentially calculates the error or loss between the predicted output and the actual target values. The goal is to minimize this cost function, which in turn improves the model's performance.
Different cost functions are used depending on the type of machine learning task and the specific requirements of the problem. For example:
- Regression tasks: Mean Squared Error (MSE) and Mean Absolute Error (MAE) are commonly used cost functions.
- Classification tasks: Cross-Entropy loss and Hinge Loss are popular choices.
The cost function is an essential component of machine learning algorithms, as it guides the learning process by providing feedback on how well the model is performing. By minimizing the cost function, the model learns to make better predictions and improve its overall accuracy.
