VALUEERROR CONTINUOUS IS NOT SUPPORTED

Continuous variable not supported in confusion matrix

You are looking at a regression model, which gives you a continous output (not classification). So when you run confusion_matrix (y_test, y_pred) it will throw the ValueError because it expected class predictions, not floating point numbers. Are you trying to predict classes, or really just a number output?

machine learning - ValueError: continuous is not supported - Data ...

1 You are getting the error because accuracy only works/is only applicable to classification problems. Since you are have a regression problem accuracy is not an applicable measure of model performance, you could try a different measure such as MSE/MAE. – Oxbowerce May 20, 2022 at 7:34 yes, there was a mistake. it's fixed. Thank you – ruchi yadav

ValueError: continuous format is not supported in ... - GitHub

Hi, it seems that the values being passed to the scorer in here are y and cv_values[:,i] are passed as y_true and y_score for roc_auc_score, but in the scorer, _ThresholdScorer takes arguments X and y which now correspond to y_true and y_score in your case are

[Solved] ValueError: continuous is not supported | 9to5Answer

ValueError: continuous is not supported python pandas scikit-learn linear-regression grid-search 22,934 Your error continuous is not supported tells me you're trying to do "something" from regression domain on classification domain. At least 1 thing captures my eye as your target is regression: scores = [ 'precision', 'recall' ] Copy

[Solved] ValueError: continuous is not supported | Solveforum

Guest. May 20, 2022. #1. ruchi yadav Asks: ValueError: continuous is not supported. I am working on a regression problem and building a model using Random Forest Regressor but while trying to get the accuracy I am getting ValueError: continuous is not supported. Code: train=pd.read_csv (r"C:\Users\DELL\OneDrive\Documents\BigMart data\Train.csv ...

ValueError: continuous is not supported #65 - GitHub

The text was updated successfully, but these errors were encountered:

[Solved] Got continuous is not supported error in | 9to5Answer

Solution 2. Since you are doing a classification task, you should be using the metric R-squared (co-effecient of determination) instead of accuracy score (accuracy score is used for classification problems). R-squared can be computed by calling score function provided by RandomForestRegressor, for example: rfr.score (X_test,Y_test)

SequentialFeatureSelector ValueError: continuous format is not supported

GridSearchCV gives ValueError: continuous is not supported for DecisionTreeRegressor; Scikit Learn RFECV ValueError: continuous is not supported; KerasRegressor: ValueError: continuous is not supported; ValueError: multiclass format is not supported , xgboost; GridSearchCV gives ValueError: continuous is not supported for DecisionTreeRegressor

Predicting time-series values with MLP and Tensorflow

Tensorflow and Batch Normalization with Batch Size==1 => Outputs all zeros. First training epoch is very slow. Evaluating (model.evaluate) with a triplet loss Siamese neural network model - tensorflow. Reusing layer weights in Tensorflow. Set "training=False" of "tf.layers.batch_normalization" when training will get a better validation result.

Solutions of multiclass format is not supported in Classification - Kaggle

ValueError: multiclass format is not supported Solutions : For this you have to set this parameter in your model objective = 'multi:softmax' XGBClassifier( n_jobs = 1, objective = 'multi:softmax', silent=1, tree_method='approx' )
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