predict.LinearModel

Case-based reasoning is a problem-solving methodology that involves solving a new problem by referring to the solution of a similar problem in a large set of previously solved problems. The key aspect of Case Based Reasoning is to determine the problem that "most closely" matches the new problem at hand. This is achieved by defining a family of distance functions and using these distance functions as parameters for local averaging regression estimates of the final result. The optimal distance function is chosen based on a specific error measure used in regression estimation. This approach allows for efficient problem-solving by leveraging past experiences and adapting solutions from similar cases. The underlying concept is inspired by the work of Dippon J. et al. (2002) <doi:10.1016/S0167-9473(02)00058-0>.

Dr. Mueller

CaseBasedReasoning

Case Based Reasoning

Simon Mueller

PD Dr. Juergen Dippon

predict.LinearModel function

<dl><dt>object</dt>
<dd>A <code>LinearModel</code> R6 object</dd>
<dt>newdata</dt>
<dd>Query data of class data.frame</dd>
<dt>k</dt>
<dd>Number of similar cases to return</dd>
<dt>...</dt>
<dd>Additional arguments (currently unused)</dd></dl>

Arguments

Predict method for LinearModel — predict.LinearModel

<dl>

<dt>object</dt>
<dd>A <code>LinearModel</code> R6 object</dd>


<dt>newdata</dt>
<dd>Query data of class data.frame</dd>


<dt>k</dt>
<dd>Number of similar cases to return</dd>


<dt>...</dt>
<dd>Additional arguments (currently unused)</dd>

</dl>

predict.LinearModel: Predict method for LinearModel

Description

Usage

Value

Arguments