# Fitted Linear Function

(Redirected from Regressed Linear Model)

A Fitted Linear Function is a linear function that is a fitted estimation function (based on a linear metamodel whose function coefficients have been set by a linear regression algorithm).

**AKA:**Regressed Linear Model.**Context:**- It can be the output of Linear Regression Task (solved by a linear regression system).

**Example(s):**- a Fitted Perceptron.
- …

**Counter-Example(s):****See:**Non-Linear Predictive Estimator, Mixtures-based Estimator.

## References

### 2015

- (Allain, 2015) ⇒ Rhett Allain. (2015). “An Ode to the Graph, Physics’ Underappreciated Workhorse.” In: Wired, 2015-06-03
- QUOTE: … if you fit a linear equation to that data, what would the slope represent? ...
- Plotting data as a linear graph is a great way to examine the validity of a model.
- Sometimes you will have to do something to the variables in order to make the plot a linear function (like squaring both sides of the model).
- The slope of the linear function that fits the data actually means something. Find the slope and find out what it represents (and check it).

- QUOTE: … if you fit a linear equation to that data, what would the slope represent? ...

### 2013

- http://en.wikipedia.org/wiki/Linear_predictor_function
- In statistics and in machine learning, a
**linear predictor function**is a linear function (linear combination) of a set of coefficients and explanatory variables (independent variables), whose value is used to predict the outcome of a dependent variable. Functions of this sort are standard in linear regression, where the coefficients are termed regression coefficients. However, they also occur in various types of linear classifiers (e.g. logistic regression, perceptrons, support vector machines, and linear discriminant analysis), as well as in various other models, such as principal component analysis and factor analysis. In many of these models, the coefficients are referred to as "weights".

- In statistics and in machine learning, a