Attempting to see the Discrimination Threshold Plot for Fitted models

Question

I'm trying to use the Discriminationthreshold Visualizer for my fitted models; They're all binary classifiers (logistic regression, lightgbm, and xgbclassifier) however, based on the documentation I am having a hard time producing the plot on already fitted models. My code is the following

# test is a logistic regression model 
from yellowbrick.classifier import DiscriminationThreshold
visualizer = DiscriminationThreshold(test, is_fitted = True)
visualizer.show()

the output of this is the following:

Can someone please help me understand how to use the discriminationthreshold properly on a fitted model. I tried with the others lgbm and xgb and got an empty plot as well.

Answer 1

The DiscriminationThreshold visualizer works as the evaluator of a model and requires evaluation data set. This means you need to fit the visualizer regardless whether your model is already fitted or not. You seem to have omitted this step because your model is already fitted.

Try something like this:

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

from yellowbrick.classifier import DiscriminationThreshold
from yellowbrick.datasets import load_spam

# Load a binary classification dataset and split
X, y = load_spam()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Instantiate and fit the LogisticRegression model
model = LogisticRegression(multi_class="auto", solver="liblinear")
model.fit(X_train, y_train)

visualizer = DiscriminationThreshold(model, is_fitted=True)
visualizer.fit(X_test, y_test)  # Fit the test data to the visualizer
visualizer.show()