Hi Guys,(adsbygoogle = window.adsbygoogle || []).push({});

I am just starting readings on machine learning and came across ways that the error can be used to learn the target function. The way I understand it,

Error: [itex] e = f(\vec{x}) - y* [/itex]

Loss: [itex] L(\vec{x}) = \frac{( f(\vec{x}) - y* )^2}{2} [/itex]

Empirical Risk: [itex] R(f) = \sum_{i=o}^{m} \frac{( f(\vec{x}) - y* )^2}{2m} [/itex]

where y* is the desired function, [itex] \vec{x} [/itex] is the sample vector (example) and m is the number of examples in your sample space.

I don't understand why the factor of 2 is present in the expression for loss. The only condition my instructor placed on loss was that it had to non-negative, hence the exponent 2. But the division by two only seems to make the loss less than it really is.

I also came across the expression for mean squared error, and it is essentially the loss without the factor of 2. If anyone could shed light on why the factor of 2 is there, I would be grateful

**Physics Forums - The Fusion of Science and Community**

Join Physics Forums Today!

The friendliest, high quality science and math community on the planet! Everyone who loves science is here!

The friendliest, high quality science and math community on the planet! Everyone who loves science is here!

# Mean Squared Error vs Loss

Can you offer guidance or do you also need help?

Draft saved
Draft deleted

Loading...

Similar Threads for Mean Squared Error | Date |
---|---|

"Shooting Method" for simulating a Particle in an Infinite Square Well | Mar 10, 2017 |

How did the term "asynchronously" come to mean ... | Dec 11, 2016 |

Fortran What does this Fortran Line Mean? | Jul 28, 2016 |

Viewing a webpage means downloading it? | Jan 16, 2016 |

**Physics Forums - The Fusion of Science and Community**