I'm trying to make a classification prediction(although multivariate regression would be better;but there are no regression node, which is a problem for another day). I created the training and testing dataset using the create dataset node. Then i trained the decision tree classifier model but there were no option to choose the target feature like for example setosa,versicolor column in the classic iris dataset. Im having a bunch of number from 20-30 each of which is a catergory(similar to setosa,versicolor but number from 20-30 instead of names) for the classifier. But training was successful. Then i tried to make prediction and i get some errors:
1)Traceback (most recent call last):
File "C:\Users\messi.node-red\node_modules\node-red-contrib-machine-learning-v2\nodes\predictor\predictor.py", line 33, in
- print(json.dumps(model.predict(features)))
File "C:\Users\messi.node-red\node_modules\node-red-contrib-machine-learning-v2\nodes\predictor/../../utils\sklw.py", line 28, in predict
- return self.model.predict(x).tolist()
File "C:\Users\messi\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\tree_classes.py", line 442, in predict
- X = self._validate_X_predict(X, check_input)
File "C:\Users\messi\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\tree_classes.py", line 407, in _validate_X_predict
5)self._check_n_features(X, reset=reset)
File "C:\Users\messi\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\base.py", line 365, in _check_n_features
- raise ValueError(
ValueError: X has 3 features, but DecisionTreeClassifier is expecting 2 features as input.
Here are my nodes:
[{"id":"b9a76546.8bf828","type":"tab","label":"reg_test","disabled":false,"info":""},{"id":"ac4682ef.6d6b38","type":"load dataset","z":"b9a76546.8bf828","name":"","datasetFolder":"D:\CSE\IOT Domain Analyst\project","datasetName":"W_Dataset","partition":"train.csv","input":true,"output":true,"x":330,"y":160,"wires":[["44848e97.b977a8"],["4b8d4621.194048"]]},{"id":"479f11d3.282c9","type":"inject","z":"b9a76546.8bf828","name":"","props":[{"p":"payload"},{"p":"topic","vt":"str"}],"repeat":"","crontab":"","once":false,"onceDelay":0.1,"topic":"","payload":"","payloadType":"date","x":120,"y":220,"wires":[["ac4682ef.6d6b38","663f0717.f445c8"]]},{"id":"1ed302cb.f7abed","type":"debug","z":"b9a76546.8bf828","name":"","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","statusVal":"","statusType":"auto","x":810,"y":80,"wires":},{"id":"4b8d4621.194048","type":"debug","z":"b9a76546.8bf828","name":"","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","statusVal":"","statusType":"auto","x":750,"y":240,"wires":},{"id":"b87d6960.35de98","type":"predictor","z":"b9a76546.8bf828","name":"","modelPath":"D:\CSE\IOT Domain Analyst\project","modelName":"D_TREE","x":500,"y":240,"wires":[["44848e97.b977a8"],["4b8d4621.194048"]]},{"id":"663f0717.f445c8","type":"load dataset","z":"b9a76546.8bf828","name":"","datasetFolder":"D:\CSE\IOT Domain Analyst\project","datasetName":"W_Dataset","partition":"test.csv","input":true,"output":true,"x":330,"y":280,"wires":[["b87d6960.35de98"],["4b8d4621.194048"]]},{"id":"44848e97.b977a8","type":"assessment","z":"b9a76546.8bf828","name":"","score":"accuracy_score","x":550,"y":80,"wires":[["1ed302cb.f7abed"],["4b8d4621.194048"]]}]
TRAIN CSV:
26,79,25
24,86,18
23,91,18
22,94,19
22,94,19
21,94,19
21,93,19
22,88,19
26,74,21
28,63,22
30,51,23
31,50,24
30,52,25
30,53,26
30,54,27
29,57,28
28,60,26
29,60,25
28,63,25
27,66,24
27,67,23
27,68,23
26,70,22
26,72,22
26,74,22
26,75,22
25,77,23
25,80,22
24,83,22
23,85,22
23,86,22
25,77,22
27,67,24
29,57,26
30,52,28
31,49,28
31,47,28
31,47,28
31,48,28
30,49,28
30,50,28
29,53,27
28,57,26
27,58,25
27,59,25
27,62,25
26,64,25
26,65,25
26,66,25
26,66,25
25,68,24
25,70,24
24,74,24
23,77,24
22,79,23
24,72,23
27,61,23
29,53,24
30,49,26
31,48,28
31,47,28
31,48,28
31,48,28
30,50,27
30,54,27
28,58,27
27,62,27
27,64,27
27,64,27
26,65,27
26,67,26
26,69,26
26,71,26
25,74,21
24,77,22
23,82,22
22,86,22
21,86,22
21,82,22
23,72,22
26,62,24
29,54,24
31,48,26
31,47,26
29,54,26
30,52,28
31,50,28
30,56,28
29,58,26
28,61,26
27,65,25
27,67,25
27,70,25
26,72,25
26,73,26
26,74,25
26,74,25
25,77,25
25,81,25
24,85,24
23,90,24
23,89,24
22,89,24
24,80,23
27,69,24
29,60,24
30,53,28
31,51,27
32,50,27
32,49,28
31,48,27
31,50,27
30,53,28
29,57,26
28,63,26
27,64,27
27,64,27
27,64,25
27,67,26
26,68,25
26,70,25
26,72,25
25,75,24
25,78,24
23,83,24
22,86,23
22,85,23
24,75,23
27,65,24
29,56,26
31,49,27
32,47,27
32,48,27
32,47,27
31,47,27
31,51,27
30,56,27
29,60,26
28,63,26
27,65,26
27,66,26
27,67,26
27,70,26
26,73,26
26,75,26
26,76,25
25,77,25
25,79,26
24,82,25
TEST CSV:
24,83,26
24,80,25
26,72,25
28,65,25
30,56,26
31,53,25
32,51,26
32,51,27
31,53,28
31,54,28
30,55,28
30,58,28
29,63,28
28,67,28
27,68,27
27,70,27
27,71,27
27,72,27
26,74,26
26,75,26
26,78,26
25,81,25
24,86,25
24,86,25
24,86,26
24,85,26
26,80,26
27,73,27
29,63,27
31,51,27
32,49,27
33,49,27
32,51,27
32,52,27
31,52,27
30,58,28
29,64,28
28,70,28
27,73,27
27,75,26
26,78,26
26,78,25
I WANT THE LAST COLUMN TO BE THE TARGET
