Difference between revisions of "TDSM 10.27"

From The Data Science Design Manual Wikia
Jump to: navigation, search
(Created page with "For an estimator to be effective, the distance between every point and its neighbors has to be on average smaller than a value <math>d</math>. In 1D, this requires the number...")
 
Line 1: Line 1:
 
For an estimator to be effective, the distance between every point and its neighbors has to be on average smaller than a value <math>d</math>. In 1D, this requires the number of training points <math>n \approx 1/d</math> points on average.
 
For an estimator to be effective, the distance between every point and its neighbors has to be on average smaller than a value <math>d</math>. In 1D, this requires the number of training points <math>n \approx 1/d</math> points on average.
  
If the number of features (number of dimension) is p, the minimum distance between 2 points is now <math>1/d^n \Rightarrow</math> the model would need <math>n^p</math> training points. As <math>p</math> increase linearly, the number of training point increases exponentially.
+
If the number of features (number of dimension) is p, the minimum distance between 2 points is now <math>d^n \Rightarrow</math> the model would need <math>n^p</math> training points. As <math>p</math> increase linearly, the number of training point increases exponentially.

Revision as of 14:29, 8 October 2017

For an estimator to be effective, the distance between every point and its neighbors has to be on average smaller than a value d. In 1D, this requires the number of training points n1/d points on average.

If the number of features (number of dimension) is p, the minimum distance between 2 points is now dn the model would need np training points. As p increase linearly, the number of training point increases exponentially.