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### valueerror continuous is not supported

The error message “ValueError: continuous is not supported” typically occurs when trying to perform an operation or function that is not compatible with continuous data.

Continuous data refers to data that can take on any value within a given range or interval, such as measurements of height, weight, or time. Some functions and operations are only valid for discrete data, which refers to data that can only take on specific, distinct values.

To resolve this error, you may need to check the data type of the variable you are working with and make sure it is appropriate for the function or operation you are trying to perform. Alternatively, you may need to consider transforming the data into a format that is compatible with the desired operation or function.

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### Continuous is not supported confusion matrix

A confusion matrix is typically used to evaluate the performance of a classification model by comparing the actual and predicted values of the target variable.

A confusion matrix is typically used for models that predict categorical outcomes, such as a binary classification problem with two classes (e.g., yes or no). However, if the target variable is continuous, then a confusion matrix may not be an appropriate evaluation metric.

For continuous target variables, other metrics like mean squared error (MSE), mean absolute error (MAE), or root mean squared error (RMSE) can be used to evaluate the performance of the model.

Therefore, if you encounter an error message saying that “Continuous is not supported in the confusion matrix”, it is likely that the target variable in your classification problem is continuous, and you should consider using a different evaluation metric, such as MSE or MAE, to evaluate the performance of your model.

### Continuous format is not supported

## Python – ValueError: continuous is not supported

I am using GridSearchCV for cross validation of a linear regression (not a classifier nor a logistic regression).

I also use StandardScaler for normalization of X

My dataframe has 17 features (X) and 5 targets (y) (observations). Around 1150 rows

I keep getting ValueError: continuous is not supported error message and ran out of options.

here is some code (assume all imports are done properly):

These are the main pieces of the code

This is the main part of Error output:

To start with, both have nothing to do with regression (as @zero323 pointed out in a comment to your question): they are accuracy measures for classification. Try any regression scores that suit your tastes from this sklearn docs page, section “3.3.1.1. Common cases: predefined values”

As far as the rest of the code is concerned, I would strongly encourage you to rewrite your code from scratch: chunk for Lasso, chunk for Ridge, chunk for ElasticNet and chunk for SVM (why would you run Ridge and Lasso separately from ElasticNet as they are special cases of ElasticNet???). This will take you no more than 10-15 lines of code. Only after you made it sure all of them execute, optimal hyperparameters are found, and desirable regression metrics are calculated I would attempt optimizing the code and putting everything together in a loop.

PS:

how are these loops supposed to run:

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- 问答- 腾讯云开发者社区-腾讯云

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