This code divides the 150 data rows into a training data set (70%) and a testing
data set (30%). The training data set is input for the NeuralNet function.
DROP TABLE IF EXISTS breast_cancer_train;
CREATE TABLE breast_cancer_train DISTRIBUTE BY hash(samplecode) AS
SELECT * from breast_cancer_data ORDER BY samplecode ASC LIMIT 489;
SELECT * FROM breast_cancer_train ORDER BY samplecode;
Alternatively, you can do the preceding task with the Sample or RandomSample function.
NeuralNet Example Train Table breast_cancer_train (Columns 1-5)
samplecode |
clumpthickness |
uniformityofcellsize |
uniformityofcellshape |
marginaladhesion |
61634 |
5 |
4 |
3 |
1 |
63375 |
9 |
1 |
2 |
6 |
76389 |
10 |
4 |
7 |
2 |
95719 |
6 |
10 |
10 |
10 |
128059 |
1 |
1 |
1 |
1 |
142932 |
7 |
6 |
10 |
5 |
144888 |
8 |
10 |
10 |
8 |
145447 |
8 |
4 |
4 |
1 |
160296 |
5 |
8 |
8 |
10 |
167528 |
4 |
1 |
1 |
1 |
169356 |
3 |
1 |
1 |
1 |
183913 |
1 |
2 |
2 |
1 |
... |
... |
... |
... |
... |
NeuralNet Example Train Table breast_cancer_train (Columns 6-11)
singleepithelialcell |
barenuclei |
blandchromatin |
normalnucleoli |
mitoses |
class |
2 |
|
2 |
3 |
1 |
2 |
4 |
10 |
7 |
7 |
2 |
4 |
2 |
8 |
6 |
1 |
1 |
4 |
8 |
10 |
7 |
10 |
7 |
4 |
2 |
5 |
5 |
1 |
1 |
2 |
3 |
10 |
9 |
10 |
2 |
4 |
5 |
10 |
7 |
8 |
1 |
4 |
2 |
9 |
3 |
3 |
1 |
4 |
5 |
10 |
8 |
10 |
3 |
4 |
2 |
1 |
3 |
6 |
1 |
2 |
2 |
|
3 |
1 |
1 |
2 |
2 |
1 |
1 |
1 |
1 |
2 |
... |
... |
... |
... |
... |
... |
DROP TABLE IF EXISTS breast_cancer_test;
CREATE TABLE breast_cancer_test DISTRIBUTE BY hash(samplecode) AS
SELECT * FROM breast_cancer_data ORDER BY samplecode DESC LIMIT 210;
SELECT * FROM breast_cancer_train ORDER BY samplecode;
NeuralNet Example Train Table breast_cancer_test (Columns 1-5)
samplecode |
clumpthickness |
uniformityofcellsize |
uniformityofcellshape |
marginaladhesion |
1222936 |
8 |
7 |
8 |
7 |
1223003 |
5 |
3 |
3 |
1 |
1223282 |
1 |
1 |
1 |
1 |
1223306 |
3 |
1 |
1 |
1 |
1223426 |
1 |
1 |
1 |
1 |
1223543 |
1 |
2 |
1 |
3 |
1223793 |
6 |
10 |
7 |
7 |
1223967 |
6 |
1 |
3 |
1 |
... |
... |
... |
... |
... |
NeuralNet Example Train Table breast_cancer_test (Columns 6-11)
singleepithelialcell |
barenuclei |
blandchromatin |
normalnucleoli |
mitoses |
class |
5 |
5 |
5 |
10 |
2 |
4 |
2 |
1 |
2 |
1 |
1 |
2 |
2 |
1 |
2 |
1 |
1 |
2 |
2 |
4 |
1 |
1 |
1 |
2 |
2 |
1 |
3 |
1 |
1 |
2 |
2 |
1 |
1 |
2 |
1 |
2 |
6 |
4 |
8 |
10 |
2 |
4 |
2 |
1 |
3 |
1 |
1 |
2 |
... |
... |
... |
... |
... |
... |