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Train a decision tree inductive learning model

Assignment Help Management Information Sys
Reference no: EM131110237

Question on data mining

Your task is to predict the output variable "choice" based on 16 input features: x1, x2, ....,x15, x16.The output "choice" is a categorical variable that can take 5 possible values: "M", "B", "J", P", and "O".The first 8 input features (x1, x2, ....,x8) are binary variables.

The last 8 input features (x9, x10, ....,x16) are continuous variables.

1. Train a decision tree inductive learning model on the data from the CSV file "finalQ3Train.csv" that contains 1500 examples.

2. Express your trained model in the form of IF ... THEN rules. Test your trained model on the 500 examples from the CSV file "finalQ3Test.csv" and present your confusion matrix.

3. Predict values for "choice" for the 8 examples in the csv file "finalQ3newCases.csv". The examples are shown below

x1

x2

x3

x4

x5

x6

x7

x8

x9

x10

x11

x12

x13

x14

x15

x16

choice

1

1

1

1

1

0

1

0

0.0284

0.2196

0.5259

0.6206

0.0950

0.3350

0.2470

0.9676

 

1

1

0

1

1

0

0

1

0.7419

0.9260

0.4711

0.8340

0.8770

0.1129

0.4805

0.7469

 

0

0

1

0

1

0

1

1

0.3867

0.9002

0.4240

0.6029

0.5547

0.6674

0.1499

0.4527

 

0

1

0

1

1

0

0

0

0.8848

0.0752

0.1195

0.3625

0.1565

0.1205

0.7666

0.4188

 

1

0

0

0

1

1

1

0

0.2893

0.0067

0.1855

0.6999

0.5777

0.5959

0.0324

0.8211

 

1

1

1

1

1

1

1

1

0.7549

0.3705

0.3349

0.8772

0.9453

0.2476

0.3782

0.1878

 

1

1

1

1

0

1

1

1

0.7921

0.1539

0.9011

0.5596

0.7125

0.1035

0.0587

0.2399

 

0

0

1

0

1

0

0

0

0.7190

0.8441

0.5841

0.8670

0.7620

0.8794

0.3351

0.4677

 

Attachment:- Assignment.rar

Reference no: EM131110237

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