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| Both sides previous revisionPrevious revision | Next revisionBoth sides next revision | ||
| data_mining:regression [2014/07/13 03:31] – [Normalengleichungen] phreazer | data_mining:regression [2014/07/13 03:36] – [Normalengleichungen] phreazer | ||
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| * Feature scaling nicht notwendig. | * Feature scaling nicht notwendig. | ||
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| + | Was wenn $X^TX$ singulär (nicht invertierbar)? | ||
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| + | (pinv in Octave) | ||
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| + | **Gründe für Singularität: | ||
| + | * Redundante features (lineare Abhängigkeit) | ||
| + | * Zu viele Features (z.B. m <= n) | ||
| + | * Lösung: Features weglassen oder regularisieren | ||
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| **Wann was benutzten? | **Wann was benutzten? | ||