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MAE,MSE and MAPE aren't comparable?


Why does an SVM model store the support vectors, and not just the separating hyperplane?Working back and forth with DataFrame and nparray in Pipeline transformersHow to iterate and modify rows in a dataframe( convert numerical to categorical)Data Mining - Intent matching and classification of textIs this the correct way to apply a recommender system based on KNN and cosine similarity to predict continuous values?Fill missing values AND normaliseUnderstanding the Shuffle and Split Process in a Neural Network Codehow to transformation of row to column and column to row in python pandas?SVM - why does scaling the parameters w and b result in nothing meaningful?













2












$begingroup$


I'm a newbie in data science. I'm working on a regression problem. I'm getting 2.5 MAPE. 400 MAE 437000 MSE. As my MAPE is quite low but why I'm getting high MSE and MAE? This is the link to my data



from sklearn.metrics import mean_absolute_error 
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import Normalizer
import matplotlib.pyplot as plt
def mean_absolute_percentage_error(y_true, y_pred):
y_true, y_pred = np.array(y_true), np.array(y_pred)
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100

import pandas as pd
from sklearn import preprocessing

features=pd.read_csv('selectedData.csv')
import numpy as np
from scipy import stats
print(features.shape)
features=features[(np.abs(stats.zscore(features)) < 3).all(axis=1)]
target = features['SYSLoad']
features= features.drop('SYSLoad', axis = 1)
names=list(features)

for i in names:
x=features[[i]].values.astype(float)
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
features[i]=x_scaled


Selecting the target Variable which want to predict and for which we are finding feature imps



import numpy as np
print(features.shape)
print(features.describe())
from sklearn.model_selection import train_test_split
train_input, test_input, train_target, test_target =
train_test_split(features, target, test_size = 0.25, random_state = 42)
trans=Normalizer().fit(train_input);
train_input=Normalizer().fit_transform(train_input);
test_input=trans.fit_transform(test_input);

n=test_target.values;
test_targ=pd.DataFrame(n);

from sklearn.svm import SVR
svr_rbf = SVR(kernel='poly', C=10, epsilon=10,gamma=10)
y_rbf = svr_rbf.fit(train_input, train_target);
predicted=y_rbf.predict(test_input);
plt.figure
plt.xlim(300,500);
print('Total Days For training',len(train_input)); print('Total Days For
Testing',len(test_input))
plt.ylabel('Load(MW) Prediction 3 '); plt.xlabel('Days');
plt.plot(test_targ,'-b',label='Actual'); plt.plot(predicted,'-r',label='POLY
kernel ');
plt.gca().legend(('Actual','RBF'))
plt.title('SVM')
plt.show();


test_target=np.array(test_target)
print(test_target)
MAPE=mean_absolute_percentage_error(test_target,predicted);
print(MAPE);
mae=mean_absolute_error(test_target,predicted)
mse=mean_squared_error(test_target, predicted)
print(mae);
print(mse);
print(test_target);
print(predicted);









share|improve this question











$endgroup$
















    2












    $begingroup$


    I'm a newbie in data science. I'm working on a regression problem. I'm getting 2.5 MAPE. 400 MAE 437000 MSE. As my MAPE is quite low but why I'm getting high MSE and MAE? This is the link to my data



    from sklearn.metrics import mean_absolute_error 
    from sklearn.metrics import mean_squared_error
    from sklearn.preprocessing import Normalizer
    import matplotlib.pyplot as plt
    def mean_absolute_percentage_error(y_true, y_pred):
    y_true, y_pred = np.array(y_true), np.array(y_pred)
    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100

    import pandas as pd
    from sklearn import preprocessing

    features=pd.read_csv('selectedData.csv')
    import numpy as np
    from scipy import stats
    print(features.shape)
    features=features[(np.abs(stats.zscore(features)) < 3).all(axis=1)]
    target = features['SYSLoad']
    features= features.drop('SYSLoad', axis = 1)
    names=list(features)

    for i in names:
    x=features[[i]].values.astype(float)
    min_max_scaler = preprocessing.MinMaxScaler()
    x_scaled = min_max_scaler.fit_transform(x)
    features[i]=x_scaled


    Selecting the target Variable which want to predict and for which we are finding feature imps



    import numpy as np
    print(features.shape)
    print(features.describe())
    from sklearn.model_selection import train_test_split
    train_input, test_input, train_target, test_target =
    train_test_split(features, target, test_size = 0.25, random_state = 42)
    trans=Normalizer().fit(train_input);
    train_input=Normalizer().fit_transform(train_input);
    test_input=trans.fit_transform(test_input);

    n=test_target.values;
    test_targ=pd.DataFrame(n);

    from sklearn.svm import SVR
    svr_rbf = SVR(kernel='poly', C=10, epsilon=10,gamma=10)
    y_rbf = svr_rbf.fit(train_input, train_target);
    predicted=y_rbf.predict(test_input);
    plt.figure
    plt.xlim(300,500);
    print('Total Days For training',len(train_input)); print('Total Days For
    Testing',len(test_input))
    plt.ylabel('Load(MW) Prediction 3 '); plt.xlabel('Days');
    plt.plot(test_targ,'-b',label='Actual'); plt.plot(predicted,'-r',label='POLY
    kernel ');
    plt.gca().legend(('Actual','RBF'))
    plt.title('SVM')
    plt.show();


    test_target=np.array(test_target)
    print(test_target)
    MAPE=mean_absolute_percentage_error(test_target,predicted);
    print(MAPE);
    mae=mean_absolute_error(test_target,predicted)
    mse=mean_squared_error(test_target, predicted)
    print(mae);
    print(mse);
    print(test_target);
    print(predicted);









    share|improve this question











    $endgroup$














      2












      2








      2





      $begingroup$


      I'm a newbie in data science. I'm working on a regression problem. I'm getting 2.5 MAPE. 400 MAE 437000 MSE. As my MAPE is quite low but why I'm getting high MSE and MAE? This is the link to my data



      from sklearn.metrics import mean_absolute_error 
      from sklearn.metrics import mean_squared_error
      from sklearn.preprocessing import Normalizer
      import matplotlib.pyplot as plt
      def mean_absolute_percentage_error(y_true, y_pred):
      y_true, y_pred = np.array(y_true), np.array(y_pred)
      return np.mean(np.abs((y_true - y_pred) / y_true)) * 100

      import pandas as pd
      from sklearn import preprocessing

      features=pd.read_csv('selectedData.csv')
      import numpy as np
      from scipy import stats
      print(features.shape)
      features=features[(np.abs(stats.zscore(features)) < 3).all(axis=1)]
      target = features['SYSLoad']
      features= features.drop('SYSLoad', axis = 1)
      names=list(features)

      for i in names:
      x=features[[i]].values.astype(float)
      min_max_scaler = preprocessing.MinMaxScaler()
      x_scaled = min_max_scaler.fit_transform(x)
      features[i]=x_scaled


      Selecting the target Variable which want to predict and for which we are finding feature imps



      import numpy as np
      print(features.shape)
      print(features.describe())
      from sklearn.model_selection import train_test_split
      train_input, test_input, train_target, test_target =
      train_test_split(features, target, test_size = 0.25, random_state = 42)
      trans=Normalizer().fit(train_input);
      train_input=Normalizer().fit_transform(train_input);
      test_input=trans.fit_transform(test_input);

      n=test_target.values;
      test_targ=pd.DataFrame(n);

      from sklearn.svm import SVR
      svr_rbf = SVR(kernel='poly', C=10, epsilon=10,gamma=10)
      y_rbf = svr_rbf.fit(train_input, train_target);
      predicted=y_rbf.predict(test_input);
      plt.figure
      plt.xlim(300,500);
      print('Total Days For training',len(train_input)); print('Total Days For
      Testing',len(test_input))
      plt.ylabel('Load(MW) Prediction 3 '); plt.xlabel('Days');
      plt.plot(test_targ,'-b',label='Actual'); plt.plot(predicted,'-r',label='POLY
      kernel ');
      plt.gca().legend(('Actual','RBF'))
      plt.title('SVM')
      plt.show();


      test_target=np.array(test_target)
      print(test_target)
      MAPE=mean_absolute_percentage_error(test_target,predicted);
      print(MAPE);
      mae=mean_absolute_error(test_target,predicted)
      mse=mean_squared_error(test_target, predicted)
      print(mae);
      print(mse);
      print(test_target);
      print(predicted);









      share|improve this question











      $endgroup$




      I'm a newbie in data science. I'm working on a regression problem. I'm getting 2.5 MAPE. 400 MAE 437000 MSE. As my MAPE is quite low but why I'm getting high MSE and MAE? This is the link to my data



      from sklearn.metrics import mean_absolute_error 
      from sklearn.metrics import mean_squared_error
      from sklearn.preprocessing import Normalizer
      import matplotlib.pyplot as plt
      def mean_absolute_percentage_error(y_true, y_pred):
      y_true, y_pred = np.array(y_true), np.array(y_pred)
      return np.mean(np.abs((y_true - y_pred) / y_true)) * 100

      import pandas as pd
      from sklearn import preprocessing

      features=pd.read_csv('selectedData.csv')
      import numpy as np
      from scipy import stats
      print(features.shape)
      features=features[(np.abs(stats.zscore(features)) < 3).all(axis=1)]
      target = features['SYSLoad']
      features= features.drop('SYSLoad', axis = 1)
      names=list(features)

      for i in names:
      x=features[[i]].values.astype(float)
      min_max_scaler = preprocessing.MinMaxScaler()
      x_scaled = min_max_scaler.fit_transform(x)
      features[i]=x_scaled


      Selecting the target Variable which want to predict and for which we are finding feature imps



      import numpy as np
      print(features.shape)
      print(features.describe())
      from sklearn.model_selection import train_test_split
      train_input, test_input, train_target, test_target =
      train_test_split(features, target, test_size = 0.25, random_state = 42)
      trans=Normalizer().fit(train_input);
      train_input=Normalizer().fit_transform(train_input);
      test_input=trans.fit_transform(test_input);

      n=test_target.values;
      test_targ=pd.DataFrame(n);

      from sklearn.svm import SVR
      svr_rbf = SVR(kernel='poly', C=10, epsilon=10,gamma=10)
      y_rbf = svr_rbf.fit(train_input, train_target);
      predicted=y_rbf.predict(test_input);
      plt.figure
      plt.xlim(300,500);
      print('Total Days For training',len(train_input)); print('Total Days For
      Testing',len(test_input))
      plt.ylabel('Load(MW) Prediction 3 '); plt.xlabel('Days');
      plt.plot(test_targ,'-b',label='Actual'); plt.plot(predicted,'-r',label='POLY
      kernel ');
      plt.gca().legend(('Actual','RBF'))
      plt.title('SVM')
      plt.show();


      test_target=np.array(test_target)
      print(test_target)
      MAPE=mean_absolute_percentage_error(test_target,predicted);
      print(MAPE);
      mae=mean_absolute_error(test_target,predicted)
      mse=mean_squared_error(test_target, predicted)
      print(mae);
      print(mse);
      print(test_target);
      print(predicted);






      data-mining pandas svm numpy






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Feb 19 at 17:40









      ebrahimi

      74221021




      74221021










      asked Feb 19 at 15:26









      imtiaz ul Hassanimtiaz ul Hassan

      204




      204




















          2 Answers
          2






          active

          oldest

          votes


















          1












          $begingroup$

          I'll be honest, I haven't thoroughly checked your code. However, I can see that the range of values of your dataset is approx [0,12000]. As an engineer, I see that:



          1. sqrt(MSE) = sqrt(437000) = 661 units.

          2. MAE = 400 units.

          3. MAPE = 2.5 which means that MAE can be up to 0.025*12000= 250 units.

          All three cases show similar magnitude of error, so I wouldn't say that "MAPE is quite low but you're getting high mse and MAE".



          Those 3 values explain the results from similar yet different perspectives. Keep in mind, if the values were all the same, there would have been no need for all 3 of those metrics to exist :)






          share|improve this answer











          $endgroup$












          • $begingroup$
            Thank you and what does r-square metrics shows?
            $endgroup$
            – imtiaz ul Hassan
            Feb 19 at 16:25











          • $begingroup$
            I believe Wikipedia has a very nice explanation :) en.wikipedia.org/wiki/Coefficient_of_determination
            $endgroup$
            – pcko1
            Feb 19 at 21:49










          • $begingroup$
            @pcko1 thank you for your answer. Is it possible to elaborate? I didn't understand what you've meat for 1 to 3. For instance, what do you mean by MAE = 400 units?
            $endgroup$
            – Media
            2 days ago










          • $begingroup$
            well MAE stands for Mean Absolute Error and OP mentioned that he gets "2.5 MAPE, 400 MAE, 437000 MSE". I just tried to make a back-of-the-envelope evaluation of the magnitute of those values, which seem reasonable given his dataset :) as for "units", this refers to the physical units of the problem, whatever they might be
            $endgroup$
            – pcko1
            2 days ago







          • 1




            $begingroup$
            Thank you :) nice perspective.
            $endgroup$
            – Media
            2 days ago


















          1












          $begingroup$

          You are stating something that is by definition the case. A Mean Absolute Percentage Error (MAPE) is typically a small number expressed in percentage, hopefully in the single digit. Meanwhile the Mean Squared Error (MSE) and Mean Absolute Error) are expressed in square of units and units respectively. If your units are > 1, the MSE could get easily very large on a relative basis compared to the MAE. And, the MAE could also be a lot larger than the MAPE. This is just like saying a nominal number is a lot larger than its log or natural logs. Your three error measures are measured on pretty different scale.



          They just bring some perspective to how well your model fit your data. Depending on the circumstances or context one error measure may be more relevant than the other.






          share|improve this answer








          New contributor




          Sympa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.






          $endgroup$












            Your Answer





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            2 Answers
            2






            active

            oldest

            votes








            2 Answers
            2






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            1












            $begingroup$

            I'll be honest, I haven't thoroughly checked your code. However, I can see that the range of values of your dataset is approx [0,12000]. As an engineer, I see that:



            1. sqrt(MSE) = sqrt(437000) = 661 units.

            2. MAE = 400 units.

            3. MAPE = 2.5 which means that MAE can be up to 0.025*12000= 250 units.

            All three cases show similar magnitude of error, so I wouldn't say that "MAPE is quite low but you're getting high mse and MAE".



            Those 3 values explain the results from similar yet different perspectives. Keep in mind, if the values were all the same, there would have been no need for all 3 of those metrics to exist :)






            share|improve this answer











            $endgroup$












            • $begingroup$
              Thank you and what does r-square metrics shows?
              $endgroup$
              – imtiaz ul Hassan
              Feb 19 at 16:25











            • $begingroup$
              I believe Wikipedia has a very nice explanation :) en.wikipedia.org/wiki/Coefficient_of_determination
              $endgroup$
              – pcko1
              Feb 19 at 21:49










            • $begingroup$
              @pcko1 thank you for your answer. Is it possible to elaborate? I didn't understand what you've meat for 1 to 3. For instance, what do you mean by MAE = 400 units?
              $endgroup$
              – Media
              2 days ago










            • $begingroup$
              well MAE stands for Mean Absolute Error and OP mentioned that he gets "2.5 MAPE, 400 MAE, 437000 MSE". I just tried to make a back-of-the-envelope evaluation of the magnitute of those values, which seem reasonable given his dataset :) as for "units", this refers to the physical units of the problem, whatever they might be
              $endgroup$
              – pcko1
              2 days ago







            • 1




              $begingroup$
              Thank you :) nice perspective.
              $endgroup$
              – Media
              2 days ago















            1












            $begingroup$

            I'll be honest, I haven't thoroughly checked your code. However, I can see that the range of values of your dataset is approx [0,12000]. As an engineer, I see that:



            1. sqrt(MSE) = sqrt(437000) = 661 units.

            2. MAE = 400 units.

            3. MAPE = 2.5 which means that MAE can be up to 0.025*12000= 250 units.

            All three cases show similar magnitude of error, so I wouldn't say that "MAPE is quite low but you're getting high mse and MAE".



            Those 3 values explain the results from similar yet different perspectives. Keep in mind, if the values were all the same, there would have been no need for all 3 of those metrics to exist :)






            share|improve this answer











            $endgroup$












            • $begingroup$
              Thank you and what does r-square metrics shows?
              $endgroup$
              – imtiaz ul Hassan
              Feb 19 at 16:25











            • $begingroup$
              I believe Wikipedia has a very nice explanation :) en.wikipedia.org/wiki/Coefficient_of_determination
              $endgroup$
              – pcko1
              Feb 19 at 21:49










            • $begingroup$
              @pcko1 thank you for your answer. Is it possible to elaborate? I didn't understand what you've meat for 1 to 3. For instance, what do you mean by MAE = 400 units?
              $endgroup$
              – Media
              2 days ago










            • $begingroup$
              well MAE stands for Mean Absolute Error and OP mentioned that he gets "2.5 MAPE, 400 MAE, 437000 MSE". I just tried to make a back-of-the-envelope evaluation of the magnitute of those values, which seem reasonable given his dataset :) as for "units", this refers to the physical units of the problem, whatever they might be
              $endgroup$
              – pcko1
              2 days ago







            • 1




              $begingroup$
              Thank you :) nice perspective.
              $endgroup$
              – Media
              2 days ago













            1












            1








            1





            $begingroup$

            I'll be honest, I haven't thoroughly checked your code. However, I can see that the range of values of your dataset is approx [0,12000]. As an engineer, I see that:



            1. sqrt(MSE) = sqrt(437000) = 661 units.

            2. MAE = 400 units.

            3. MAPE = 2.5 which means that MAE can be up to 0.025*12000= 250 units.

            All three cases show similar magnitude of error, so I wouldn't say that "MAPE is quite low but you're getting high mse and MAE".



            Those 3 values explain the results from similar yet different perspectives. Keep in mind, if the values were all the same, there would have been no need for all 3 of those metrics to exist :)






            share|improve this answer











            $endgroup$



            I'll be honest, I haven't thoroughly checked your code. However, I can see that the range of values of your dataset is approx [0,12000]. As an engineer, I see that:



            1. sqrt(MSE) = sqrt(437000) = 661 units.

            2. MAE = 400 units.

            3. MAPE = 2.5 which means that MAE can be up to 0.025*12000= 250 units.

            All three cases show similar magnitude of error, so I wouldn't say that "MAPE is quite low but you're getting high mse and MAE".



            Those 3 values explain the results from similar yet different perspectives. Keep in mind, if the values were all the same, there would have been no need for all 3 of those metrics to exist :)







            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited Feb 19 at 21:57

























            answered Feb 19 at 15:58









            pcko1pcko1

            1,581417




            1,581417











            • $begingroup$
              Thank you and what does r-square metrics shows?
              $endgroup$
              – imtiaz ul Hassan
              Feb 19 at 16:25











            • $begingroup$
              I believe Wikipedia has a very nice explanation :) en.wikipedia.org/wiki/Coefficient_of_determination
              $endgroup$
              – pcko1
              Feb 19 at 21:49










            • $begingroup$
              @pcko1 thank you for your answer. Is it possible to elaborate? I didn't understand what you've meat for 1 to 3. For instance, what do you mean by MAE = 400 units?
              $endgroup$
              – Media
              2 days ago










            • $begingroup$
              well MAE stands for Mean Absolute Error and OP mentioned that he gets "2.5 MAPE, 400 MAE, 437000 MSE". I just tried to make a back-of-the-envelope evaluation of the magnitute of those values, which seem reasonable given his dataset :) as for "units", this refers to the physical units of the problem, whatever they might be
              $endgroup$
              – pcko1
              2 days ago







            • 1




              $begingroup$
              Thank you :) nice perspective.
              $endgroup$
              – Media
              2 days ago
















            • $begingroup$
              Thank you and what does r-square metrics shows?
              $endgroup$
              – imtiaz ul Hassan
              Feb 19 at 16:25











            • $begingroup$
              I believe Wikipedia has a very nice explanation :) en.wikipedia.org/wiki/Coefficient_of_determination
              $endgroup$
              – pcko1
              Feb 19 at 21:49










            • $begingroup$
              @pcko1 thank you for your answer. Is it possible to elaborate? I didn't understand what you've meat for 1 to 3. For instance, what do you mean by MAE = 400 units?
              $endgroup$
              – Media
              2 days ago










            • $begingroup$
              well MAE stands for Mean Absolute Error and OP mentioned that he gets "2.5 MAPE, 400 MAE, 437000 MSE". I just tried to make a back-of-the-envelope evaluation of the magnitute of those values, which seem reasonable given his dataset :) as for "units", this refers to the physical units of the problem, whatever they might be
              $endgroup$
              – pcko1
              2 days ago







            • 1




              $begingroup$
              Thank you :) nice perspective.
              $endgroup$
              – Media
              2 days ago















            $begingroup$
            Thank you and what does r-square metrics shows?
            $endgroup$
            – imtiaz ul Hassan
            Feb 19 at 16:25





            $begingroup$
            Thank you and what does r-square metrics shows?
            $endgroup$
            – imtiaz ul Hassan
            Feb 19 at 16:25













            $begingroup$
            I believe Wikipedia has a very nice explanation :) en.wikipedia.org/wiki/Coefficient_of_determination
            $endgroup$
            – pcko1
            Feb 19 at 21:49




            $begingroup$
            I believe Wikipedia has a very nice explanation :) en.wikipedia.org/wiki/Coefficient_of_determination
            $endgroup$
            – pcko1
            Feb 19 at 21:49












            $begingroup$
            @pcko1 thank you for your answer. Is it possible to elaborate? I didn't understand what you've meat for 1 to 3. For instance, what do you mean by MAE = 400 units?
            $endgroup$
            – Media
            2 days ago




            $begingroup$
            @pcko1 thank you for your answer. Is it possible to elaborate? I didn't understand what you've meat for 1 to 3. For instance, what do you mean by MAE = 400 units?
            $endgroup$
            – Media
            2 days ago












            $begingroup$
            well MAE stands for Mean Absolute Error and OP mentioned that he gets "2.5 MAPE, 400 MAE, 437000 MSE". I just tried to make a back-of-the-envelope evaluation of the magnitute of those values, which seem reasonable given his dataset :) as for "units", this refers to the physical units of the problem, whatever they might be
            $endgroup$
            – pcko1
            2 days ago





            $begingroup$
            well MAE stands for Mean Absolute Error and OP mentioned that he gets "2.5 MAPE, 400 MAE, 437000 MSE". I just tried to make a back-of-the-envelope evaluation of the magnitute of those values, which seem reasonable given his dataset :) as for "units", this refers to the physical units of the problem, whatever they might be
            $endgroup$
            – pcko1
            2 days ago





            1




            1




            $begingroup$
            Thank you :) nice perspective.
            $endgroup$
            – Media
            2 days ago




            $begingroup$
            Thank you :) nice perspective.
            $endgroup$
            – Media
            2 days ago











            1












            $begingroup$

            You are stating something that is by definition the case. A Mean Absolute Percentage Error (MAPE) is typically a small number expressed in percentage, hopefully in the single digit. Meanwhile the Mean Squared Error (MSE) and Mean Absolute Error) are expressed in square of units and units respectively. If your units are > 1, the MSE could get easily very large on a relative basis compared to the MAE. And, the MAE could also be a lot larger than the MAPE. This is just like saying a nominal number is a lot larger than its log or natural logs. Your three error measures are measured on pretty different scale.



            They just bring some perspective to how well your model fit your data. Depending on the circumstances or context one error measure may be more relevant than the other.






            share|improve this answer








            New contributor




            Sympa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
            Check out our Code of Conduct.






            $endgroup$

















              1












              $begingroup$

              You are stating something that is by definition the case. A Mean Absolute Percentage Error (MAPE) is typically a small number expressed in percentage, hopefully in the single digit. Meanwhile the Mean Squared Error (MSE) and Mean Absolute Error) are expressed in square of units and units respectively. If your units are > 1, the MSE could get easily very large on a relative basis compared to the MAE. And, the MAE could also be a lot larger than the MAPE. This is just like saying a nominal number is a lot larger than its log or natural logs. Your three error measures are measured on pretty different scale.



              They just bring some perspective to how well your model fit your data. Depending on the circumstances or context one error measure may be more relevant than the other.






              share|improve this answer








              New contributor




              Sympa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.






              $endgroup$















                1












                1








                1





                $begingroup$

                You are stating something that is by definition the case. A Mean Absolute Percentage Error (MAPE) is typically a small number expressed in percentage, hopefully in the single digit. Meanwhile the Mean Squared Error (MSE) and Mean Absolute Error) are expressed in square of units and units respectively. If your units are > 1, the MSE could get easily very large on a relative basis compared to the MAE. And, the MAE could also be a lot larger than the MAPE. This is just like saying a nominal number is a lot larger than its log or natural logs. Your three error measures are measured on pretty different scale.



                They just bring some perspective to how well your model fit your data. Depending on the circumstances or context one error measure may be more relevant than the other.






                share|improve this answer








                New contributor




                Sympa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.






                $endgroup$



                You are stating something that is by definition the case. A Mean Absolute Percentage Error (MAPE) is typically a small number expressed in percentage, hopefully in the single digit. Meanwhile the Mean Squared Error (MSE) and Mean Absolute Error) are expressed in square of units and units respectively. If your units are > 1, the MSE could get easily very large on a relative basis compared to the MAE. And, the MAE could also be a lot larger than the MAPE. This is just like saying a nominal number is a lot larger than its log or natural logs. Your three error measures are measured on pretty different scale.



                They just bring some perspective to how well your model fit your data. Depending on the circumstances or context one error measure may be more relevant than the other.







                share|improve this answer








                New contributor




                Sympa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.









                share|improve this answer



                share|improve this answer






                New contributor




                Sympa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.









                answered 2 days ago









                SympaSympa

                1164




                1164




                New contributor




                Sympa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.





                New contributor





                Sympa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.






                Sympa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.



























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