Difference between revisions of "TDSM 10.27"
From The Data Science Design Manual Wikia
(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...") |
|||
(One intermediate revision by the same user not shown) | |||
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> | + | If the number of features (number of dimension) is p, the minimum distance between 2 points is now <math>d^p \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. |
Latest 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 [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]d^p \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.