Given a CSV file containing a dataset of continuous feature information with a binary class label, this program will train off of the given dataset using the IC3 algorithm and predict the accuracy on the same dataset.
There is a total of 4 synthetic training datasets and a pokemon dataset. The synthetic training datasets do not have any feature labels assigned to them, but the pokemon labels are as follows:
Total,HP,Attack,Defense,Sp. Atk,Sp. Def,Speed,Generation,Type 1_Bug,Type 1_Dark,Type 1_Dragon,Type 1_Electric,Type 1_Fairy,Type 1_Fighting,Type 1_Fire,Type 1_Flying,Type 1_Ghost,Type 1_Grass,Type 1_Ground,Type 1_Ice,Type 1_Normal,Type 1_Poison,Type 1_Psychic,Type 1_Rock,Type 1_Steel,Type 1_Water,Type 2_Bug,Type 2_Dark,Type 2_Dragon,Type 2_Electric,Type 2_Fairy,Type 2_Fighting,Type 2_Fire,Type 2_Flying,Type 2_Ghost,Type 2_Grass,Type 2_Ground,Type 2_Ice,Type 2_Normal,Type 2_Poison,Type 2_Psychic,Type 2_Rock,Type 2_Steel,Type 2_Water, Legendary
The prediction for the pokemon dataset is if the pokemon is Legendary or not. Please note the dataset has been artificially altered to give more data to test with.
The results for the prediction on each dataset with 5 bins:
Predicted with an accuracy of 100.0% for synthetic-1 dataset.
Predicted with an accuracy of 94.5% for synthetic-2 dataset.
Predicted with an accuracy of 87.0% for synthetic-3 dataset.
Predicted with an accuracy of 96.0% for synthetic-4 dataset.
Predicted with an accuracy of 95.44% for pokemon dataset.