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y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])
x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])
y = np.asanyarray(test[['CO2EMISSIONS']])
it causes the bottom warning:
/usr/local/lib/python3.8/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but LinearRegression was fitted without feature names
warnings.warn( ...
replace with
x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])
y = np.asanyarray(test[['CO2EMISSIONS']])
y_hat= regr.predict(x)
The text was updated successfully, but these errors were encountered:
in ML0101EN-Reg-Simple-Linear-Regression-Co2
y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])
x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])
y = np.asanyarray(test[['CO2EMISSIONS']])
it causes the bottom warning:
/usr/local/lib/python3.8/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but LinearRegression was fitted without feature names
warnings.warn( ...
replace with
x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])
y = np.asanyarray(test[['CO2EMISSIONS']])
y_hat= regr.predict(x)
The text was updated successfully, but these errors were encountered: