NameError: name 'label_scalers' is not defined

This error occurs when you try to reference a variable or object that has not been defined or has been deleted. In this case, the variable 'label_scalers' has not been defined in the current scope or has been deleted before it is being referenced. To resolve this error, you need to define the variable 'label_scalers' before referring to it.

相关问题

evaluate() missing 2 required positional arguments: 'test_y' and 'label_scalers'

As an AI language model, I do not have access to the specific code you are referring to. However, based on the error message you provided, it seems that the function `evaluate()` in your code requires two positional arguments, namely `test_y` and `label_scalers`, which are not being provided when the function is called. You may need to pass these arguments to the function when calling it in your code.

x_train = scaler.fit_transform(x_train)

This line of code is using the `fit_transform` method of the `scaler` object to scale the `x_train` data.

The `fit_transform` method is a convenient way to first fit the scaler to the data (i.e. calculate the mean and standard deviation of the data) and then transform the data using the calculated parameters.

The `scaler` object is typically an instance of a class from the `sklearn.preprocessing` module, such as `StandardScaler`, `MinMaxScaler`, or `RobustScaler`. These scalers are commonly used to preprocess data for machine learning algorithms by scaling features to have zero mean and unit variance or scaling features to a specific range.

In this case, `scaler.fit_transform(x_train)` is scaling the `x_train` data using the `fit_transform` method of the `scaler` object. The scaled data is then assigned back to `x_train`.