WebFeb 10, 2024 · SKLearn TypeError: got an unexpected keyword argument 'as_frame' 1. pred = model.predict_classes([prepare(file_path)]) AttributeError: 'Functional' object has no attribute 'predict_classes' 1. ValueError: inconsistent shapes … WebJul 22, 2024 · 1 You can add a title when using plot_confusion_matrix as follows: clf = SVC (random_state=0) clf.fit (X_train, y_train) cm = plot_confusion_matrix (clf, X_test, y_test) cm.ax_.set_title ('Confusion Matrix') Share Improve this answer Follow answered Jul 24, 2024 at 15:46 Soroush Faridan 170 4 8 Add a comment -1
TypeError: sum() got an unexpected keyword argument
WebOct 17, 2024 · accuracy_score should have no keyword argument average, that's true. Are you sure your second error message comes from the accuracy_score function? I … WebMar 22, 2024 · Hi @silkski, we changed what some of the compute_loss functions do internally in a relatively recent version of Transformers.As a result, if you’re using an older version, the example scripts for the current version won’t work for you. Try updating on your local machine (e.g. pip install --upgrade transformers) and let us know if that fixes it! pinchuk kevin spacey
TypeError: sum() got an unexpected keyword argument …
WebMethod 2: Using **kwargs argument. The **kwargs argument (which stands for k ey w ord arg ument s) is a special argument that can be used in Python to pass various arguments to a Python function. The function turns unexpected keyword arguments into a dictionary that can be accessed inside the function. WebOct 1, 2024 · plot_confusion_matrix() doesn't appear to accept an axis argument. However the documentation does indicate that you can pass in an axis. I am trying to plot on an existing figure into subplots, but getting this error: TypeError: plot_confusion_matrix() got an unexpected keyword argument 'ax' WebMay 20, 2016 · You get an exception because UserDefinedFunction.__call__ supports only varargs and not keyword args. def __call__ (self, *cols): sc = SparkContext._active_spark_context jc = self._judf.apply (_to_seq (sc, cols, _to_java_column)) return Column (jc) pinchurchin