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overfam

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The answer OVERFAM has 0 possible clue(s) in existing crosswords.

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The word OVERFAM is NOT valid in any word game. (Sorry, you cannot play OVERFAM in Scrabble, Words With Friends etc)

There are 7 letters in OVERFAM ( A1E1F4M3O1R1V4 )

To search all scrabble anagrams of OVERFAM, to go: OVERFAM?

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Scrabble results that can be created with an extra letter added to OVERFAM

WAVEFORM

6 letters out of OVERFAM

FEMORA FOAMER

5 letters out of OVERFAM

AFORE FAVOR FORAM FORME FOVEA FRAME MORAE MOVER VOMER

4 letters out of OVERFAM

AERO ARVO AVER FAME FARE FARM FARO FAVE FEAR FOAM FORA FORE FORM FRAE FROE FROM MARE MORA MORE MOVE OMER OVER RAVE REAM ROAM ROVE VERA

3 letters out of OVERFAM

ARE ARF ARM AVE AVO EAR ERA FAR FEM FER FOE FOR FRO MAE MAR MOA MOR OAF OAR ORA ORE OVA RAM REF REM REV ROE ROM VAR VOE

2 letters out of OVERFAM

AE AM AR EF EM ER FA FE MA ME MO OE OF OM OR RE

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Definitions of overfam in various dictionaries:

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Overfam might refer to
Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented).
* Oversampling and undersampling are opposite and roughly equivalent techniques. They both involve using a bias to select more samples from one class than from another.
* The usual reason for oversampling is to correct for a bias in the original dataset. One scenario
* where it is useful is when training a classifier using labelled training data from a biased source, since
* labelled training data is valuable but often comes from un-representative sources.
* For example, suppose we have a sample of 1000 people of which 66.7% are male. We know the general population is 50% female, and we may wish to adjust our dataset to represent this. Simple oversampling will select each female example twice, and this copying will produce a balanced dataset of 1333 samples with 50% female. Simple undersampling will drop some of the male samples at random to give a balanced dataset of 667 samples, again with 50% female.There are also more complex oversampling techniques, including the creation of artificial data points .
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