In general, We can define the Random Forest (RF) as collection of classifier that involves of multi Decision Trees (DTs). In general each data set divided in two subsets, the first take 2\3 of the total dataset called bagging while, the second subset takes the remind 1\3 of the total datasett called out-of bag. Non-Parametric Random Forests croups of trees increased from bagging data. In classification process, the trees are jointed by widely polling with one poll for each tree completed all the trees in the forest. For regression, forests are formed by dive number of polling for each group on the total number of polling for all tree.

Let your'database have the following values

Species Sepal length Sepal width Petal length Petal width

Setosa 5.1 3.5 1.4 0.2

Setosa 4.9 3 1.4 0.2

Setosa 4.7 3.2 1.3 0.2

Setosa 4.6 3.1 1.5 0.2

Setosa 5 3.6 1.4 0.2

Setosa 5.4 3.9 1.7 0.4

Setosa 4.6 3.4 1.4 0.3

Setosa 5 3.4 1.5 0.2

Setosa 4.4 2.9 1.4 0.2

Setosa 4.9 3.1 1.5 0.1

Setosa 5.4 3.7 1.5 0.2

Setosa 4.8 3.4 1.6 0.2

Setosa 4.8 3 1.4 0.1

Setosa 4.3 3 1.1 0.1

Setosa 5.8 4 1.2 0.2

Setosa 5.7 4.4 1.5 0.4

Setosa 5.4 3.9 1.3 0.4

Setosa 5.1 3.5 1.4 0.3

Setosa 5.7 3.8 1.7 0.3

Setosa 5.1 3.8 1.5 0.3

Setosa 5.4 3.4 1.7 0.2

Setosa 5.1 3.7 1.5 0.4

Setosa 4.6 3.6 1 0.2

Setosa 5.1 3.3 1.7 0.5

Setosa 4.8 3.4 1.9 0.2

Setosa 5 3 1.6 0.2

Setosa 5 3.4 1.6 0.4

Setosa 5.2 3.5 1.5 0.2

Setosa 5.2 3.4 1.4 0.2

Setosa 4.7 3.2 1.6 0.2

Setosa 4.8 3.1 1.6 0.2

Setosa 5.4 3.4 1.5 0.4

Setosa 5.2 4.1 1.5 0.1

Setosa 5.5 4.2 1.4 0.2

Setosa 4.9 3.1 1.5 0.2

Setosa 5 3.2 1.2 0.2

Setosa 5.5 3.5 1.3 0.2

Setosa 4.9 3.6 1.4 0.1

Setosa 4.4 3 1.3 0.2

Setosa 5.1 3.4 1.5 0.2

Versicolor 7 3.2 4.7 1.4

Versicolor 6.4 3.2 4.5 1.5

Versicolor 6.9 3.1 4.9 1.5

Versicolor 5.5 2.3 4 1.3

Versicolor 6.5 2.8 4.6 1.5

Versicolor 5.7 2.8 4.5 1.3

Versicolor 6.3 3.3 4.7 1.6

Versicolor 4.9 2.4 3.3 1

Versicolor 6.6 2.9 4.6 1.3

Versicolor 5.2 2.7 3.9 1.4

Versicolor 5 2 3.5 1

Versicolor 5.9 3 4.2 1.5

Versicolor 6 2.2 4 1

Versicolor 6.1 2.9 4.7 1.4

Versicolor 5.6 2.9 3.6 1.3

Versicolor 6.7 3.1 4.4 1.4

Versicolor 5.6 3 4.5 1.5

Versicolor 5.8 2.7 4.1 1

Versicolor 6.2 2.2 4.5 1.5

Versicolor 5.6 2.5 3.9 1.1

Versicolor 5.9 3.2 4.8 1.8

Versicolor 6.1 2.8 4 1.3

Versicolor 6.3 2.5 4.9 1.5

Versicolor 6.1 2.8 4.7 1.2

Versicolor 6.4 2.9 4.3 1.3

Versicolor 6.6 3 4.4 1.4

Versicolor 6.8 2.8 4.8 1.4

Versicolor 6.7 3 5 1.7

Versicolor 6 2.9 4.5 1.5

Versicolor 5.7 2.6 3.5 1

Versicolor 5.5 2.4 3.8 1.1

Versicolor 5.5 2.4 3.7 1

Versicolor 5.8 2.7 3.9 1.2

Versicolor 6 2.7 5.1 1.6

Versicolor 5.4 3 4.5 1.5

Versicolor 6 3.4 4.5 1.6

Versicolor 6.7 3.1 4.7 1.5

Versicolor 6.3 2.3 4.4 1.3

Versicolor 5.6 3 4.1 1.3

Versicolor 5.5 2.5 4 1.3

Virginica 6.3 3.3 6 2.5

Virginica 5.8 2.7 5.1 1.9

Virginica 7.1 3 5.9 2.1

Virginica 6.3 2.9 5.6 1.8

Virginica 6.5 3 5.8 2.2

Virginica 7.6 3 6.6 2.1

Virginica 4.9 2.5 4.5 1.7

Virginica 7.3 2.9 6.3 1.8

Virginica 6.7 2.5 5.8 1.8

Virginica 7.2 3.6 6.1 2.5

Virginica 6.5 3.2 5.1 2

Virginica 6.4 2.7 5.3 1.9

Virginica 6.8 3 5.5 2.1

Virginica 5.7 2.5 5 2

Virginica 5.8 2.8 5.1 2.4

Virginica 6.4 3.2 5.3 2.3

Virginica 6.5 3 5.5 1.8

Virginica 7.7 3.8 6.7 2.2

Virginica 7.7 2.6 6.9 2.3

Virginica 6 2.2 5 1.5

Virginica 6.9 3.2 5.7 2.3

Virginica 5.6 2.8 4.9 2

Virginica 7.7 2.8 6.7 2

Virginica 6.3 2.7 4.9 1.8

Virginica 6.7 3.3 5.7 2.1

Virginica 7.2 3.2 6 1.8

Virginica 6.2 2.8 4.8 1.8

Virginica 6.1 3 4.9 1.8

Virginica 6.4 2.8 5.6 2.1

Virginica 7.2 3 5.8 1.6

Virginica 7.4 2.8 6.1 1.9

Virginica 7.9 3.8 6.4 2

Virginica 6.4 2.8 5.6 2.2

Virginica 6.3 2.8 5.1 1.5

Virginica 6.1 2.6 5.6 1.4

Virginica 7.7 3 6.1 2.3

Virginica 6.3 3.4 5.6 2.4

Virginica 6.4 3.1 5.5 1.8

Virginica 6 3 4.8 1.8

Virginica 6.9 3.1 5.4 2.1

Virginica 6.7 3.1 5.6 2.4

Setosa 5 3.5 1.3 0.3

Setosa 4.5 2.3 1.3 0.3

Setosa 4.4 3.2 1.3 0.2

Setosa 5 3.5 1.6 0.6

Setosa 5.1 3.8 1.9 0.4

Setosa 4.8 3 1.4 0.3

Setosa 5.1 3.8 1.6 0.2

Setosa 4.6 3.2 1.4 0.2

Setosa 5.3 3.7 1.5 0.2

Setosa 5 3.3 1.4 0.2

Versicolor 5.5 2.6 4.4 1.2

Versicolor 6.1 3 4.6 1.4

Versicolor 5.8 2.6 4 1.2

Versicolor 5 2.3 3.3 1

Versicolor 5.6 2.7 4.2 1.3

Versicolor 5.7 3 4.2 1.2

Versicolor 5.7 2.9 4.2 1.3

Versicolor 6.2 2.9 4.3 1.3

Versicolor 5.1 2.5 3 1.1

Versicolor 5.7 2.8 4.1 1.3

Virginica 6.9 3.1 5.1 2.3

Virginica 5.8 2.7 5.1 1.9

Virginica 6.8 3.2 5.9 2.3

Virginica 6.7 3.3 5.7 2.5

Virginica 6.7 3 5.2 2.3

Virginica 6.3 2.5 5 1.9

Virginica 6.5 3 5.2 2

Virginica 6.2 3.4 5.4 2.3

Virginica 5.9 3 5.1 1.8

Then we can use any association rules technique such as(Aprior, Frequency pattern growth(FP-Growth), or FP-KC), by experiment you can get the following results:

Rule1 If PETAL_LENGTH Se 2.45 Then class eq Setosa

probClass1 eq 1

probClass2 eq 0

probClass3 eq 0

Rule2 If PETAL_LENGTH Lt 2.45 and PETAL_LENGTH Se 4.95 and PETAL_WIDTH Se 1.65 Then class eq Versicolor

probClass1 eq 0

probClass2 eq 1

probClass3 eq 0

Rule3 If PETAL_LENGTH Lt 2.45 and PETAL_LENGTH Se 4.95 and PETAL_WIDTH Lt 1.65 and PETAL_WIDTH Se 1.75 Then class eq Virginica

probClass1 eq 0

probClass2 eq 0

probClass3 eq 1

Rule4 If PETAL_WIDTH Se 1.75 and PETAL_LENGTH Lt 4.95 Then class eq Virginica

probClass1 eq 0

probClass2 eq 0.333333

probClass3 eq 0.666667

Rule5 If PETAL_LENGTH Lt 2.45 and PETAL_WIDTH Lt 1.75 Then class eq Virginica

probClass1 eq 0

probClass2 eq 0.0217391

probClass3 eq 0.978261

as a result, five rule describe the above Iris database, and cover all the class.

Best Regard.

Samaher

Thanks a lot!