Copy the ID3.py file.
From the commad promt the run the command
python ID3.py L K trainingSetFilePath validationSetFilePath testSetFilePath toPrint
The arguments represent the following
L, K - The parameters for the post pruning algorithm
trainingSetFilePath - The path to the csv file containing training data
validationSetFilePath - The path to the csv file containing the validation set
testSetFilePath - The path to the csv file containing the test set
toPrint - Whether to print the decision tree or not
| DataSet | Heuristic | Accuracy |
|---|---|---|
| 1 | Information Gain | 75.85 |
| 1 | Variance Impurity | 75.35 |
| 1 | Information Gain | 72.33 |
| 1 | Variance Impurity | 72.5 |
| DataSet | L | K | Heuristic | Accuracy |
|---|---|---|---|---|
| 1 | 10 | 10 | "Information Gain" | 76.35 |
| 1 | 10 | 10 | "Variance Impurity" | 76.95 |
| 1 | 10 | 20 | "Information Gain" | 76.6 |
| 1 | 10 | 20 | "Variance Impurity" | 75.6 |
| 1 | 20 | 10 | "Information Gain" | 76.6 |
| 1 | 20 | 10 | "Variance Impurity" | 75.95 |
| 1 | 30 | 30 | "Information Gain" | 75.2 |
| 1 | 30 | 30 | "Variance Impurity" | 75.35 |
| 1 | 50 | 25 | "Information Gain" | 76.85 |
| 1 | 50 | 25 | "Variance Impurity" | 77.0 |
| 1 | 60 | 40 | "Information Gain" | 76.05 |
| 1 | 60 | 40 | "Variance Impurity" | 76.65 |
| 1 | 60 | 70 | "Information Gain" | 76.85 |
| 1 | 60 | 70 | "Variance Impurity" | 76.35 |
| 1 | 75 | 75 | "Information Gain" | 77.0 |
| 1 | 75 | 75 | "Variance Impurity" | 75.3 |
| 1 | 80 | 80 | "Information Gain" | 75.2 |
| 1 | 80 | 80 | "Variance Impurity" | 76.35 |
| 1 | 100 | 100 | "Information Gain" | 77.35 |
| 1 | 100 | 100 | "Variance Impurity" | 77.25 |
| 2 | 10 | 10 | "Information Gain" | 73.17 |
| 2 | 10 | 10 | "Variance Impurity" | 72.67 |
| 2 | 10 | 20 | "Information Gain" | 72.33 |
| 2 | 10 | 20 | "Variance Impurity" | 74.17 |
| 2 | 20 | 10 | "Information Gain" | 74.0 |
| 2 | 20 | 10 | "Variance Impurity" | 73.0 |
| 2 | 30 | 30 | "Information Gain" | 74.0 |
| 2 | 30 | 30 | "Variance Impurity" | 72.5 |
| 2 | 50 | 25 | "Information Gain" | 72.5 |
| 2 | 50 | 25 | "Variance Impurity" | 73.33 |
| 2 | 60 | 40 | "Information Gain" | 74.33 |
| 2 | 60 | 40 | "Variance Impurity" | 74.5 |
| 2 | 60 | 70 | "Information Gain" | 71.67 |
| 2 | 60 | 70 | "Variance Impurity" | 71.5 |
| 2 | 75 | 75 | "Information Gain" | 71.5 |
| 2 | 75 | 75 | "Variance Impurity" | 73.67 |
| 2 | 80 | 80 | "Information Gain" | 71.5 |
| 2 | 80 | 80 | "Variance Impurity" | 73.17 |
| 2 | 100 | 100 | "Information Gain" | 72.67 |
| 2 | 100 | 100 | "Variance Impurity" | 73.83 |