Loss Functions for Linear Regression
Why are loss functions required in linear regression calculations?
A quantifiable indicator of a model’s performance during training is given by loss functions. The model may increase its accuracy and forecast more accurately by reducing the loss function and adjusting its parameters accordingly.
How can I choose the best loss function for the data I have?
A: The kind of data you have and the issue you’re attempting to address will determine the loss function you choose. Huber Loss combines the advantages of both MSE and MAE, and is a popular and effective model optimization technique. MAE is also resistant to outliers. Your choice will be guided by experimentation and an awareness of the trade-offs associated with each loss function.
How is the Huber Loss different from the MSE in handling outliers?
A: Beyond a threshold ($\delta$), the Huber Loss grows linearly instead of quadratically. As a result, it is less susceptible to significant mistakes, or outliers, than the mean square error (MSE), which squares errors and increases their effect on the loss amount.
Is it possible to design my own unique loss function?
A: It is possible to create unique loss functions to meet certain needs. Tailored loss functions have the ability to integrate subject expertise, manage complex data structures, and accommodate distinct assessment standards. However, mathematical optimization and a solid grasp of the issue area are often necessary for developing a meaningful bespoke loss function.
Loss function for Linear regression in Machine Learning
The loss function quantifies the disparity between the prediction value and the actual value. In the case of linear regression, the aim is to fit a linear equation to the observed data, the loss function evaluate the difference between the predicted value and true values. By minimizing this difference, the model strives to find the best-fitting line that captures the relationship between the input features and the target variable.
In this article, we will discuss Mean Squared Error (MSE) , Mean Absolute Error (MAE) and Huber Loss.
Table of Content
- Mean Squared Error (MSE)
- Computing Mean Squared Error in Python
- Computing Mean Squared Error using Sklearn Library
- Mean Absolute Error (MAE)
- Computing Mean Absolute Error in Python
- Computing Mean Absolute Error using Sklearn
- Huber Loss
- Computing Huber Loss in Python
- Comparison of Loss Functions for Linear Regression
- FAQs on Loss Functions for Linear Regression
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