Continuous Is Not Supported Linear Regression at Erik Suzuki blog

Continuous Is Not Supported Linear Regression. 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: I am using linear regression algorithm for a data set. I can't comment, so i'll answer: Continuous is not supported when tried to use cross_val_score and linear regression This error usually occurs when you attempt to use sklearn to fit a classification. Does it make sense to you that a continuous type is not supported? The confusion matrix is used to check discrete results, but linear regression model returns predicted result as a continuous values. In ordinary linear regression the response variable need not to be continuous, your assumption is not: Essentially, the error is telling us that the type of the target variable is continuous which is not compatible with the specific model we are trying to fit (i.e. Did you expect the input to have those characteristics? And trying to compute confusion matrix between y_pred and y_test. $$ y = β_0 + β_1x $$ but is:

Continuous is not supported when tried to use cross_val_score and linear regression The confusion matrix is used to check discrete results, but linear regression model returns predicted result as a continuous values. I am using linear regression algorithm for a data set. This error usually occurs when you attempt to use sklearn to fit a classification. Did you expect the input to have those characteristics? $$ y = β_0 + β_1x $$ but is: I can't comment, so i'll answer: 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: In ordinary linear regression the response variable need not to be continuous, your assumption is not: And trying to compute confusion matrix between y_pred and y_test.

Fixed and MixedEffects Regression Models in R

Continuous Is Not Supported Linear Regression The confusion matrix is used to check discrete results, but linear regression model returns predicted result as a continuous values. Does it make sense to you that a continuous type is not supported? Essentially, the error is telling us that the type of the target variable is continuous which is not compatible with the specific model we are trying to fit (i.e. 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: And trying to compute confusion matrix between y_pred and y_test. I am using linear regression algorithm for a data set. In ordinary linear regression the response variable need not to be continuous, your assumption is not: $$ y = β_0 + β_1x $$ but is: This error usually occurs when you attempt to use sklearn to fit a classification. I can't comment, so i'll answer: The confusion matrix is used to check discrete results, but linear regression model returns predicted result as a continuous values. Did you expect the input to have those characteristics? Continuous is not supported when tried to use cross_val_score and linear regression