我正在尝试将DiscriminationthresholdVisualizer 用于我的拟合模型；它们都是二元分类器（逻辑回归、lightgbm 和 xgbclassifier）但是，根据文档，我很难在已经拟合的模型上生成图。我的代码如下
# test is a logistic regression model
from yellowbrick.classifier import DiscriminationThreshold
visualizer = DiscriminationThreshold(test, is_fitted = True)
visualizer.show()
其输出如下：
有人可以帮助我了解如何在拟合模型上正确使用歧视阈值。我尝试了其他 lgbm 和 xgb 并得到了一个空的情节。
DiscriminationThreshold可视化器充当模型的评估器，需要评估数据集。这意味着无论您的模型是否已安装，您都需要安装可视化器。您似乎省略了这一步，因为您的模型已经安装好了。
尝试这样的事情：
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()
