Difference between revisions of "TDSM 10.27"
From The Data Science Design Manual Wikia
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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>d^ | + | 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.
