Implementation of mean squared error (MSE) metricWhich of the following is a correct implementation of mean squared error (MSE) metric?Note: numpy library has been imported as np.def MSE(real_target, predicted_target):return np.mean((np.square(real_target) – np.square(predicted_target)))def MSE(real_target, predicted_target):return np.mean((real_target – predicted_target)**2)def MSE(real_target, predicted_target):return np.sqrt(np.mean((np.square(real_target) – np.square(predicted_target))))None of the above
Question
Implementation of mean squared error (MSE) metricWhich of the following is a correct implementation of mean squared error (MSE) metric?Note: numpy library has been imported as np.def MSE(real_target, predicted_target):return np.mean((np.square(real_target) – np.square(predicted_target)))def MSE(real_target, predicted_target):return np.mean((real_target – predicted_target)**2)def MSE(real_target, predicted_target):return np.sqrt(np.mean((np.square(real_target) – np.square(predicted_target))))None of the above
Solution
The correct implementation of mean squared error (MSE) metric is:
def MSE(real_target, predicted_target): return np.mean((real_target – predicted_target)**2)
This function calculates the difference between the real target and the predicted target, squares the result (to ensure all values are positive), and then takes the mean of these squared differences. This is the standard formula for calculating the mean squared error in statistics and machine learning.
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Mean Squared Error (MSE): 2150461.9855868304 Root Mean Squared Error (RMSE): 1466.4453571772904 R-squared (R²) Score: -0.005936692333339044 Intercept: 49252.18987390094 Coefficients: Feature Coefficient Importer_quality 5.619887 Exporter_quality -3.609950 Year 1.588036 Agreement -11.532711 Product_category_Category_B -128.423976 Product_category_Category_C -134.246852 Product_category_Category_D -115.542548 Country_origin_Country_Y 150.971723 Country_origin_Country_Z 137.026169 Country_destination_Country_J 26.563896 Country_destination_Country_K 120.114008 Country_destination_Country_L 22.291822 Shipping_method_Rail 3.701013 Shipping_method_Road 7.591860 Shipping_method_Road_ 528.303054 Shipping_method_Sea 19.100443 Shipping_method_Sea_ -105.760755 May i know why the code is missing target variables mentioned below: 1) Product_category = Category_A 2) Country_origin = Country_X 3) Country_destination = Country_I 4) Shipping_method = Air
What is the relationship between MSE and the precision of a model?
Which of the following is the right option to compute the Root mean squared error (RMSE) of the forecasting model?Answer choicesSelect only one optionREVISITrmse_test=mean_squared_error(df_test['demand'],df_test['Predicted'],squared=True)rmse_test=mean_squared_error(df_test['demand'],df_test['Predicted'])rmse_test=mean_squared_error(df_test['demand'],df_test['Predicted'],squared=False)rmse_test=mean_squared_error(df_test['demand'],df_test['Predicted'],square=False)
s the formula for the mean square error?Review Later
You need to evaluate a classification model. Which metric can you use? Mean squared error (MSE)PrecisionSilhouette
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