## decision tree and one part used for testing its ability
## to classify correctly. This selection of nine parts for
## training and one part for testing is carried out in all
## of the ten different possible ways.
## A script like this can also be used to test the
## appropriateness of your choices for the constructor
## parameters entropy_threshold, max_depth_desired, and
## symbolic_to_numeric_cardinality_threshold.
import DecisionTree
import sys
#training_datafile = "training.csv"
#training_datafile = "training2.csv"
training_datafile = "training3.csv"
eval_data = DecisionTree.EvalTrainingData(
training_datafile = training_datafile,
csv_class_column_index = 1,
csv_columns_for_features = [2,3],
entropy_threshold = 0.01,
max_depth_desired = 3,
symbolic_to_numeric_cardinality_threshold = 10,
)
eval_data.get_training_data()
eval_data.evaluate_training_data()
## contained in the file `stage3cancer.csv'.
## Through the class EvalTrainingData as shown below, this script runs a 10-fold
## cross-validation test on the training data. This test divides all of the
## training data into ten parts, with nine parts used for training a decision tree
## and one part used for testing its ability to classify correctly. This selection
## of nine parts for training and one part for testing is carried out in all of the
## ten different possible ways.
## A script like this can also be used to test the appropriateness of your choices
## for the constructor parameters entropy_threshold, max_depth_desired, and
## symbolic_to_numeric_cardinality_threshold.
import DecisionTree
import sys
training_datafile = "stage3cancer.csv"
eval_data = DecisionTree.EvalTrainingData(
training_datafile=training_datafile,
csv_class_column_index=2,
csv_columns_for_features=[3, 4, 5, 6, 7, 8],
entropy_threshold=0.01,
max_depth_desired=5,
symbolic_to_numeric_cardinality_threshold=10,
csv_cleanup_needed=1,
)
eval_data.get_training_data()
eval_data.evaluate_training_data()
