Loss Function
In this article, I will discuss about Loss function, its importance and how it’s different from the cost function. Before reading this article I will suggest reading about cost function from one of my previous articles “All you need to know about Linear Regression (Theory)”.
What is Loss Function?
The loss function captures the difference between actual and predicted values for a single record. It is like a scorekeeper in a game. In ML, the “game” is training a model to make predictions, and the loss function tells how “wrong” the model is or how far off the predicted values are from the actual values.
It outputs a numerical value that represents how bad the model’s predictions are. Lower scores are better because they mean the predictions are closer to the actual values.
In a nutshell, the loss function helps to understand how “wrong” the model is and guides us to make improvements.
Loss Function vs Cost Function
- Loss Function calculates the error for a single record.
- This is like a single-player score in a game. It measures the error only for one example.
- Example: If you are predicting house prices, the loss function tells you how far off your prediction was for a particular house.
- Cost Function aggregates these errors to evaluate the model’s overall performance.
- Now, imagine a whole team playing, and you want to see how well everyone did together. The cost function is the average of the loss functions over all the training examples.
- It gives a broader view of the model’s performance.
In short, the loss function is concerned with the individual errors, while the cost function aggregates the errors to evaluate the model’s predictions.
Why Loss Functions are used?
Loss functions play a crucial role in ML and are used for various important reasons:
1: Performance Measurement
- It is used to measure the performance of the model. It gives a quantitative value that reflects how wrong the model is.
- By calculating the loss, we can compare different models or different versions of the same model to check which one performs better.
- It also helps to monitor the improvement of the model as it learns from the data. A decreasing value indicates that the model is learning and getting better.
2: Optimization
- A lot of optimization algorithms, like gradient descent use the loss function to update the model’s parameters.
- The loss function provides feedback on how to adjust the model’s parameters to improve the predictions.
3: Model Training and Learning
- By minimizing the loss. the model learns the relationship in the data.
- A well-optimized model (i.e., one with a low loss) is better able to generalize from the training data to new, unseen data.
4: Error Analysis
- If the loss is very low on the training data but high on the validation data, the model might be overfitting, meaning it’s too closely tailored to the training data and does not generalize well. (Overfitting vs Underfitting)
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