Predicting all zeros












0














I've depeloped a neural network for classification and I'm getting a 0.93 of accuracy, the problem is that I'm predicting all zeros because the distribution of the data.



Data distribution



How can I fix it? Should I change from neural network to another algorithm?



Thanks in advance



Edit: i've just checked and my model is predicting the same probability for each row.



The model is a NN with 5 layers, and tf.nn.relu6 as activation function. The cost function is tf.nn.sigmoid_cross_entropy_with_logits



To predict the values I use:



predicted = tf.nn.sigmoid(Z5)

correct_pred = tf.equal(tf.round(predicted), Y)

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))


EDIT 2



I have 'fixed' the imbalance class problem (undersampling and upsampling 0s and 1s) but the net is still predicting the same values for each row:



Prediction



I have tested to change activation function to tanh or sigmoid but then outputs NaN's






python tensorflow machine-learning deep-learning







share|improve this question




















  • 2




    Please supply info on what model are you using, what is the data, and the code.
    – Dinari
    Nov 18 '18 at 7:50










  • Google "class imbalance"...
    – desertnaut
    Nov 18 '18 at 8:10










  • In practise I have seen two possible fixes for that (although I dislike both of them). The first is to throw away parts of the dataset so that all outcomes are equally likely. The other one is to put some samples - namely those with a prediction outcome that is not occuring to often - multiple times into the dataset. With both ways all prediction results become about equally likely in the traning set. If someone knows another way I would be interested in it too.
    – quant
    Nov 18 '18 at 8:10






  • 1




    @quent class imbalance is a huge subtopic, and there are are several other approaches, like creating artificial samples of the minority class; look for SMOTE & RUSBoost
    – desertnaut
    Nov 18 '18 at 8:16
















0














I've depeloped a neural network for classification and I'm getting a 0.93 of accuracy, the problem is that I'm predicting all zeros because the distribution of the data.



Data distribution



How can I fix it? Should I change from neural network to another algorithm?



Thanks in advance



Edit: i've just checked and my model is predicting the same probability for each row.



The model is a NN with 5 layers, and tf.nn.relu6 as activation function. The cost function is tf.nn.sigmoid_cross_entropy_with_logits



To predict the values I use:



predicted = tf.nn.sigmoid(Z5)

correct_pred = tf.equal(tf.round(predicted), Y)

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))


EDIT 2



I have 'fixed' the imbalance class problem (undersampling and upsampling 0s and 1s) but the net is still predicting the same values for each row:



Prediction



I have tested to change activation function to tanh or sigmoid but then outputs NaN's






python tensorflow machine-learning deep-learning







share|improve this question




















  • 2




    Please supply info on what model are you using, what is the data, and the code.
    – Dinari
    Nov 18 '18 at 7:50










  • Google "class imbalance"...
    – desertnaut
    Nov 18 '18 at 8:10










  • In practise I have seen two possible fixes for that (although I dislike both of them). The first is to throw away parts of the dataset so that all outcomes are equally likely. The other one is to put some samples - namely those with a prediction outcome that is not occuring to often - multiple times into the dataset. With both ways all prediction results become about equally likely in the traning set. If someone knows another way I would be interested in it too.
    – quant
    Nov 18 '18 at 8:10






  • 1




    @quent class imbalance is a huge subtopic, and there are are several other approaches, like creating artificial samples of the minority class; look for SMOTE & RUSBoost
    – desertnaut
    Nov 18 '18 at 8:16














0












0








0


1





I've depeloped a neural network for classification and I'm getting a 0.93 of accuracy, the problem is that I'm predicting all zeros because the distribution of the data.



Data distribution



How can I fix it? Should I change from neural network to another algorithm?



Thanks in advance



Edit: i've just checked and my model is predicting the same probability for each row.



The model is a NN with 5 layers, and tf.nn.relu6 as activation function. The cost function is tf.nn.sigmoid_cross_entropy_with_logits



To predict the values I use:



predicted = tf.nn.sigmoid(Z5)

correct_pred = tf.equal(tf.round(predicted), Y)

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))


EDIT 2



I have 'fixed' the imbalance class problem (undersampling and upsampling 0s and 1s) but the net is still predicting the same values for each row:



Prediction



I have tested to change activation function to tanh or sigmoid but then outputs NaN's






python tensorflow machine-learning deep-learning







share|improve this question















I've depeloped a neural network for classification and I'm getting a 0.93 of accuracy, the problem is that I'm predicting all zeros because the distribution of the data.



Data distribution



How can I fix it? Should I change from neural network to another algorithm?



Thanks in advance



Edit: i've just checked and my model is predicting the same probability for each row.



The model is a NN with 5 layers, and tf.nn.relu6 as activation function. The cost function is tf.nn.sigmoid_cross_entropy_with_logits



To predict the values I use:



predicted = tf.nn.sigmoid(Z5)

correct_pred = tf.equal(tf.round(predicted), Y)

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))


EDIT 2



I have 'fixed' the imbalance class problem (undersampling and upsampling 0s and 1s) but the net is still predicting the same values for each row:



Prediction



I have tested to change activation function to tanh or sigmoid but then outputs NaN's






python tensorflow machine-learning deep-learning




python tensorflow machine-learning deep-learning






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 18 '18 at 9:40







A. Esquivias

















asked Nov 18 '18 at 7:42









A. EsquiviasA. Esquivias

485




485








  • 2




    Please supply info on what model are you using, what is the data, and the code.
    – Dinari
    Nov 18 '18 at 7:50










  • Google "class imbalance"...
    – desertnaut
    Nov 18 '18 at 8:10










  • In practise I have seen two possible fixes for that (although I dislike both of them). The first is to throw away parts of the dataset so that all outcomes are equally likely. The other one is to put some samples - namely those with a prediction outcome that is not occuring to often - multiple times into the dataset. With both ways all prediction results become about equally likely in the traning set. If someone knows another way I would be interested in it too.
    – quant
    Nov 18 '18 at 8:10






  • 1




    @quent class imbalance is a huge subtopic, and there are are several other approaches, like creating artificial samples of the minority class; look for SMOTE & RUSBoost
    – desertnaut
    Nov 18 '18 at 8:16














  • 2




    Please supply info on what model are you using, what is the data, and the code.
    – Dinari
    Nov 18 '18 at 7:50










  • Google "class imbalance"...
    – desertnaut
    Nov 18 '18 at 8:10










  • In practise I have seen two possible fixes for that (although I dislike both of them). The first is to throw away parts of the dataset so that all outcomes are equally likely. The other one is to put some samples - namely those with a prediction outcome that is not occuring to often - multiple times into the dataset. With both ways all prediction results become about equally likely in the traning set. If someone knows another way I would be interested in it too.
    – quant
    Nov 18 '18 at 8:10






  • 1




    @quent class imbalance is a huge subtopic, and there are are several other approaches, like creating artificial samples of the minority class; look for SMOTE & RUSBoost
    – desertnaut
    Nov 18 '18 at 8:16








2




2




Please supply info on what model are you using, what is the data, and the code.
– Dinari
Nov 18 '18 at 7:50




Please supply info on what model are you using, what is the data, and the code.
– Dinari
Nov 18 '18 at 7:50












Google "class imbalance"...
– desertnaut
Nov 18 '18 at 8:10




Google "class imbalance"...
– desertnaut
Nov 18 '18 at 8:10












In practise I have seen two possible fixes for that (although I dislike both of them). The first is to throw away parts of the dataset so that all outcomes are equally likely. The other one is to put some samples - namely those with a prediction outcome that is not occuring to often - multiple times into the dataset. With both ways all prediction results become about equally likely in the traning set. If someone knows another way I would be interested in it too.
– quant
Nov 18 '18 at 8:10




In practise I have seen two possible fixes for that (although I dislike both of them). The first is to throw away parts of the dataset so that all outcomes are equally likely. The other one is to put some samples - namely those with a prediction outcome that is not occuring to often - multiple times into the dataset. With both ways all prediction results become about equally likely in the traning set. If someone knows another way I would be interested in it too.
– quant
Nov 18 '18 at 8:10




1




1




@quent class imbalance is a huge subtopic, and there are are several other approaches, like creating artificial samples of the minority class; look for SMOTE & RUSBoost
– desertnaut
Nov 18 '18 at 8:16




@quent class imbalance is a huge subtopic, and there are are several other approaches, like creating artificial samples of the minority class; look for SMOTE & RUSBoost
– desertnaut
Nov 18 '18 at 8:16












1 Answer
1






active

oldest

votes


















0














There are multiple solutions for unbalanced data. But first, the accuracy is not a good metric for unbalanced data, because if you only had 5 positives and 95 negatives, you accuracy will be 95% of predicting negatives. You should check sensitivity and specificity, or other metrics that work good with unbalanced data like the LIFT score.



To train the model with unbalanced data, there are multiple solutions. One of them is the Up-sample Minority Class.




Up-sampling is the process of randomly duplicating observations from
the minority class in order to reinforce its signal.




You can upsample data with a code like this:



from sklearn.utils import resample

# Separate majority and minority classes

df_majority = df[df.balance==0]

df_minority = df[df.balance==1]



# Upsample minority class

df_minority_upsampled = resample(df_minority, 

                                 replace=True,     # sample with replacement

                                 n_samples=576,    # to match majority class

                                 random_state=123) # reproducible results



# Combine majority class with upsampled minority class

df_upsampled = pd.concat([df_majority, df_minority_upsampled])



# Display new class counts

df_upsampled.balance.value_counts()

# 1    576

# 0    576

# Name: balance, dtype: int64


You can find more information and other solutions that are well explained here.






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    There are multiple solutions for unbalanced data. But first, the accuracy is not a good metric for unbalanced data, because if you only had 5 positives and 95 negatives, you accuracy will be 95% of predicting negatives. You should check sensitivity and specificity, or other metrics that work good with unbalanced data like the LIFT score.



    To train the model with unbalanced data, there are multiple solutions. One of them is the Up-sample Minority Class.




    Up-sampling is the process of randomly duplicating observations from
    the minority class in order to reinforce its signal.




    You can upsample data with a code like this:



    from sklearn.utils import resample
    
# Separate majority and minority classes
    
df_majority = df[df.balance==0]
    
df_minority = df[df.balance==1]
    

    
# Upsample minority class
    
df_minority_upsampled = resample(df_minority, 
    
                                 replace=True,     # sample with replacement
    
                                 n_samples=576,    # to match majority class
    
                                 random_state=123) # reproducible results
    

    
# Combine majority class with upsampled minority class
    
df_upsampled = pd.concat([df_majority, df_minority_upsampled])
    

    
# Display new class counts
    
df_upsampled.balance.value_counts()
    
# 1    576
    
# 0    576
    
# Name: balance, dtype: int64


    You can find more information and other solutions that are well explained here.






    share|improve this answer


























      0














      There are multiple solutions for unbalanced data. But first, the accuracy is not a good metric for unbalanced data, because if you only had 5 positives and 95 negatives, you accuracy will be 95% of predicting negatives. You should check sensitivity and specificity, or other metrics that work good with unbalanced data like the LIFT score.



      To train the model with unbalanced data, there are multiple solutions. One of them is the Up-sample Minority Class.




      Up-sampling is the process of randomly duplicating observations from
      the minority class in order to reinforce its signal.




      You can upsample data with a code like this:



      from sklearn.utils import resample
      
# Separate majority and minority classes
      
df_majority = df[df.balance==0]
      
df_minority = df[df.balance==1]
      

      
# Upsample minority class
      
df_minority_upsampled = resample(df_minority, 
      
                                 replace=True,     # sample with replacement
      
                                 n_samples=576,    # to match majority class
      
                                 random_state=123) # reproducible results
      

      
# Combine majority class with upsampled minority class
      
df_upsampled = pd.concat([df_majority, df_minority_upsampled])
      

      
# Display new class counts
      
df_upsampled.balance.value_counts()
      
# 1    576
      
# 0    576
      
# Name: balance, dtype: int64


      You can find more information and other solutions that are well explained here.






      share|improve this answer
























        0












        0








        0






        There are multiple solutions for unbalanced data. But first, the accuracy is not a good metric for unbalanced data, because if you only had 5 positives and 95 negatives, you accuracy will be 95% of predicting negatives. You should check sensitivity and specificity, or other metrics that work good with unbalanced data like the LIFT score.



        To train the model with unbalanced data, there are multiple solutions. One of them is the Up-sample Minority Class.




        Up-sampling is the process of randomly duplicating observations from
        the minority class in order to reinforce its signal.




        You can upsample data with a code like this:



        from sklearn.utils import resample
        
# Separate majority and minority classes
        
df_majority = df[df.balance==0]
        
df_minority = df[df.balance==1]
        

        
# Upsample minority class
        
df_minority_upsampled = resample(df_minority, 
        
                                 replace=True,     # sample with replacement
        
                                 n_samples=576,    # to match majority class
        
                                 random_state=123) # reproducible results
        

        
# Combine majority class with upsampled minority class
        
df_upsampled = pd.concat([df_majority, df_minority_upsampled])
        

        
# Display new class counts
        
df_upsampled.balance.value_counts()
        
# 1    576
        
# 0    576
        
# Name: balance, dtype: int64


        You can find more information and other solutions that are well explained here.






        share|improve this answer












        There are multiple solutions for unbalanced data. But first, the accuracy is not a good metric for unbalanced data, because if you only had 5 positives and 95 negatives, you accuracy will be 95% of predicting negatives. You should check sensitivity and specificity, or other metrics that work good with unbalanced data like the LIFT score.



        To train the model with unbalanced data, there are multiple solutions. One of them is the Up-sample Minority Class.




        Up-sampling is the process of randomly duplicating observations from
        the minority class in order to reinforce its signal.




        You can upsample data with a code like this:



        from sklearn.utils import resample
        
# Separate majority and minority classes
        
df_majority = df[df.balance==0]
        
df_minority = df[df.balance==1]
        

        
# Upsample minority class
        
df_minority_upsampled = resample(df_minority, 
        
                                 replace=True,     # sample with replacement
        
                                 n_samples=576,    # to match majority class
        
                                 random_state=123) # reproducible results
        

        
# Combine majority class with upsampled minority class
        
df_upsampled = pd.concat([df_majority, df_minority_upsampled])
        

        
# Display new class counts
        
df_upsampled.balance.value_counts()
        
# 1    576
        
# 0    576
        
# Name: balance, dtype: int64


        You can find more information and other solutions that are well explained here.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 18 '18 at 11:42









        ManriqueManrique

        498112




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