def ml_get_zoo_tree(train_size=0.75, max_depth=5, random_state=245245):
# Load the zoo data
dataset = pd.read_csv(os.path.join(os.path.dirname(__file__), "data", "zoo.csv"))
# Drop the animal names since this is not a good feature to split the data on
dataset = dataset.drop("animal_name", axis=1)
# Split the data into a training and a testing set
features = dataset.drop("class", axis=1)
targets = dataset["class"]
train_features, test_features, train_targets, test_targets = \
train_test_split(features, targets, train_size=train_size, random_state=random_state)
# Train the model
tree = DecisionTreeClassifier(criterion="entropy", max_depth=max_depth)
tree = tree.fit(train_features, train_targets)
# Add the feature names to the tree for use in predict function
tree._feature_names = features.columns
return tree
def ml_get_zoo_tree(train_size=0.75, max_depth=5, random_state=245245):
# Load the zoo data
dataset = pd.read_csv(
os.path.join(os.path.dirname(__file__), "data", "zoo.csv"))
# Drop the animal names since this is not a good feature to split the data on
dataset = dataset.drop("animal_name", axis=1)
# Split the data into a training and a testing set
features = dataset.drop("class", axis=1)
targets = dataset["class"]
train_features, test_features, train_targets, test_targets = \
train_test_split(features, targets, train_size=train_size, random_state=random_state)
# Train the model
tree = DecisionTreeClassifier(criterion="entropy", max_depth=max_depth)
tree = tree.fit(train_features, train_targets)
# Add the feature names to the tree for use in predict function
tree._feature_names = features.columns
return tree
