How to use random forest method
    10 visualizaciones (últimos 30 días)
  
       Mostrar comentarios más antiguos
    
Hi, Below is my training data (v1,v2,v3 are process variables, and Y is the response variable,
Based on training data, given set of new v1,v2,v3, and predict Y. I want to make prediction using "Random forest tree bag" (decisiotn tree regression) method.
Training data:
    v1      v2    v3     Y
    16.00  21.05  25.01  8
    14.44  22.79  27.02  1
    14.69  21.83  25.10  1
    15.32  21.09  28.35  4
    15.22  23.38  28.11  2
    14.6   22.77  27.8   3
    15.52  22.33  27.78  0
    15.30  22.28  28.18  0
    15.47  20.94  28.14  5
    14.04  23.41  25.22  9 
    15.67  20.89  28.97  8
    14.14  20.73  26.22  6
    14.80  20.87  25.82  2
    14.63  22.33  27.03  0
    15.66  22.22  28.22  1
    14.40  20.73  28.13  7
    14.09  21.04  26.25  5
    14.61  23.48  26.24  4
    14.63  22.33  27.03  0
    15.53  22.79  28.37  6
    14.51  22.78  28.8   5
    15.76  23.27  26.62  3
    15.42  21.68  27.59  0  
    Make prediction for below two set of test data, what Y
test data:
      v1      v2     v3
      15.29  21.39  27.07      
      14.53  23.18   26.31
0 comentarios
Respuestas (1)
  Bernhard Suhm
    
 el 2 de Abr. de 2018
        You could read your data into the Classification Learner app (New Session - from File), and then train a "Bagged Tree" on it (that's how we refer to random forests). However, given how small this data set is, the performance will be terrible.
3 comentarios
  Javaid Iqbal
 el 4 de Jul. de 2018
				Sir may you please help me regarding SVM classification. When I use predict test data I'm unable predict thr right solutions.
    yfit=trainedModel.predictFcn(T);
I got result yfit=0 as predicted but when it should give yfit=1 result for tru value but this showing same yfit=0
  Jan Startek
 el 17 de Mzo. de 2021
				Does Bagged Tree really use a ranfom forest algorithm (  Breiman random forest? ). My point is I don't see where in the code a random variable selection for each node in grown trees is perforemd. TreeBagger (https://se.mathworks.com/help/stats/treebagger.html#bvf7_tc-1) seems to have such an option with the use of "'NumPredictorsToSample'" however I can't find an analogus option in TreeBagger. 
My question boils down to, is there a random variable selection performed at each split in the grown trees in the Bagged Trees algorithm in clasifiactionLearner?
Ver también
Categorías
				Más información sobre Classification Ensembles en Help Center y File Exchange.
			
	Productos
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!