Category Archives: WEKA

WEKA Rushdi Shams Track

In 3rd video it explains some of the details about different results output comes. It’s important.

In 4th video blue is yes and red is no

In 5th video it’s explained about the testing and training in details so it must be watched.

In 7th video K fold 10 means 10 different models for 10 different folds

In 8th we have tried the IRIS data

In 9th feature selection methods where attribute can be selected for different algorithms and results may vary. (Wrapper method)
feauture selection means attribute selection

In 10th ranker algotihms uses for ranking features or attributes wrapper method for machine learning tasks where filter method useful for data mining tasks

WEKA

@relation weather

@attribute outlook {sunny, overcast, rainy}
@attribute temperature numeirc
@attribute humidity numeric
@attribute windy {TRUE,FALSE}
@attribute play {yes,no}

@data
sunny, 90, 77, TRUE, no
overcast, 88, 90, FALSE, no