decision tree algorithm base class problem

[pplonski]

Code to reproduce

from supervised.automl import AutoML
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load iris dataset
data = load_iris()
X = data.data
y = data.target

# Split the dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize AutoML with default settings (Explain mode)
automl = AutoML()

# Fit the model
automl.fit(X_train, y_train)

# Make predictions
predictions = automl.predict(X_test)

# Evaluate the model
score = automl.score(X_test, y_test)

predictions, score

Error message:

DecisionTreeAlgorithm should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.
Problem during computing permutation importance. Skipping ...
2_DecisionTree logloss 0.587687 trained in 0.84 seconds