y = data.is_spam.as_matrix()
// Instantiate the classification model and visualizer
logistic = LogisticRegression()
visualizer = ThreshViz(logistic)
visualizer.fit(X, y) // Fit the training data to the visualizer
g = visualizer.poof(outpath="images/thresholdviz.png") // Draw/show/poof the data
After Change
from functools import partial
BASE = os.path.join("..", "..", "..", os.path.dirname("__file__"))
EXAMPLES = os.path.join(BASE, "examples", "data")
// TODO: Make these examples part of the code base
CHURN_DATASET = os.path.join(EXAMPLES, "churn", "churn.txt")
