logistic regression : highly sensitive model Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar Manara 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsAccuracy improvement for logistic regression modelApplication of validated logistic regression model on new dataHow much should I pay attention to the f1 score on this case?Evaluating Logistic Regression Model in TensorflowSimple logistic regression wrong predictionsQuestion about Logistic RegressionLogistic Regression Independent Sampleslogistic regressionLogistic regression in pythonHow can I measure the reliability of the specificity of a model with very small train, test, and validation datasets?
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logistic regression : highly sensitive model
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsAccuracy improvement for logistic regression modelApplication of validated logistic regression model on new dataHow much should I pay attention to the f1 score on this case?Evaluating Logistic Regression Model in TensorflowSimple logistic regression wrong predictionsQuestion about Logistic RegressionLogistic Regression Independent Sampleslogistic regressionLogistic regression in pythonHow can I measure the reliability of the specificity of a model with very small train, test, and validation datasets?
$begingroup$
I am a newbie to data science and ML. I am working on a classification problem where the task is to predict loan status (granted/not granted).
I am running a logistic regression model on the data. The accuracy of my model is 82%. However, my model is more sensitive (sensitivity = 97%) and less specific(specificity = 53%).
I want to increase the model's specificity. At this stage, after referring to a bunch of internet resources, I am confused about how to proceed.
Below is my observation :
In Testing data,
a percentage of 1's in the class label is 73.17073170731707.
Testing data has more 1's than 0's in the class label. Is this the reason behind model being highly sensitive.
I am attaching my data file and code file. Please take a look at it.
Data sample :
Process :
Data --> missing value imputation -->distribution analysis-->log transformation for normal distribution ---> one hot encoding --> feature selection --> splitting data --> model selection and evaluation
Code snippets :
Here I have selected "3 best features": Credit History, Property Area
How should I proceed? Any help (even if it's just a kick in the right direction) would be appreciated.
machine-learning logistic-regression
asked Apr 6 at 16:21
blueWingsblueWings
133
$endgroup$
add a comment |
$begingroup$
I am a newbie to data science and ML. I am working on a classification problem where the task is to predict loan status (granted/not granted).
I am running a logistic regression model on the data. The accuracy of my model is 82%. However, my model is more sensitive (sensitivity = 97%) and less specific(specificity = 53%).
I want to increase the model's specificity. At this stage, after referring to a bunch of internet resources, I am confused about how to proceed.
Below is my observation :
In Testing data,
a percentage of 1's in the class label is 73.17073170731707.
Testing data has more 1's than 0's in the class label. Is this the reason behind model being highly sensitive.
I am attaching my data file and code file. Please take a look at it.
Data sample :
Process :
Data --> missing value imputation -->distribution analysis-->log transformation for normal distribution ---> one hot encoding --> feature selection --> splitting data --> model selection and evaluation
Code snippets :
Here I have selected "3 best features": Credit History, Property Area
How should I proceed? Any help (even if it's just a kick in the right direction) would be appreciated.
machine-learning logistic-regression
asked Apr 6 at 16:21
blueWingsblueWings
133
$endgroup$
$begingroup$
Can you share some instances of the data? Perhaps there are more appropriate models than a logistic regression. Also you can weight the loss caused by 1's more heavily.
$endgroup$
– JahKnows
Apr 6 at 18:03
$begingroup$
The Google Drive link will sooner or later be dead. Keep in mind that your question may be useful for somebody in the future. So, could you please add some sample lines of your data and the relevant code snippets to your question.
$endgroup$
– georg_un
Apr 6 at 18:38
1
$begingroup$
@georg_un I have updated the question.
$endgroup$
– blueWings
Apr 6 at 19:11
$begingroup$
@JahKnows I have tried SVM with RBF kernel. But still, I am getting the same sensitivity and specificity. Also, I didn't get intuition behind weighting the loss by 1's
$endgroup$
– blueWings
Apr 6 at 19:30
add a comment |
$begingroup$
I am a newbie to data science and ML. I am working on a classification problem where the task is to predict loan status (granted/not granted).
I am running a logistic regression model on the data. The accuracy of my model is 82%. However, my model is more sensitive (sensitivity = 97%) and less specific(specificity = 53%).
I want to increase the model's specificity. At this stage, after referring to a bunch of internet resources, I am confused about how to proceed.
Below is my observation :
In Testing data,
a percentage of 1's in the class label is 73.17073170731707.
Testing data has more 1's than 0's in the class label. Is this the reason behind model being highly sensitive.
I am attaching my data file and code file. Please take a look at it.
Data sample :
Process :
Data --> missing value imputation -->distribution analysis-->log transformation for normal distribution ---> one hot encoding --> feature selection --> splitting data --> model selection and evaluation
Code snippets :
Here I have selected "3 best features": Credit History, Property Area
How should I proceed? Any help (even if it's just a kick in the right direction) would be appreciated.
machine-learning logistic-regression
asked Apr 6 at 16:21
blueWingsblueWings
133
$endgroup$
I am a newbie to data science and ML. I am working on a classification problem where the task is to predict loan status (granted/not granted).
I am running a logistic regression model on the data. The accuracy of my model is 82%. However, my model is more sensitive (sensitivity = 97%) and less specific(specificity = 53%).
I want to increase the model's specificity. At this stage, after referring to a bunch of internet resources, I am confused about how to proceed.
Below is my observation :
In Testing data,
a percentage of 1's in the class label is 73.17073170731707.
Testing data has more 1's than 0's in the class label. Is this the reason behind model being highly sensitive.
I am attaching my data file and code file. Please take a look at it.
Data sample :
Process :
Data --> missing value imputation -->distribution analysis-->log transformation for normal distribution ---> one hot encoding --> feature selection --> splitting data --> model selection and evaluation
Code snippets :
Here I have selected "3 best features": Credit History, Property Area
How should I proceed? Any help (even if it's just a kick in the right direction) would be appreciated.
machine-learning logistic-regression
machine-learning logistic-regression
asked Apr 6 at 16:21
blueWingsblueWings
133
asked Apr 6 at 16:21
blueWingsblueWings
133
edited Apr 6 at 19:07
blueWings
asked Apr 6 at 16:21
blueWingsblueWings
133
asked Apr 6 at 16:21
blueWingsblueWings
133
asked Apr 6 at 16:21
blueWingsblueWings
133
133
$begingroup$
Can you share some instances of the data? Perhaps there are more appropriate models than a logistic regression. Also you can weight the loss caused by 1's more heavily.
$endgroup$
– JahKnows
Apr 6 at 18:03
$begingroup$
The Google Drive link will sooner or later be dead. Keep in mind that your question may be useful for somebody in the future. So, could you please add some sample lines of your data and the relevant code snippets to your question.
$endgroup$
– georg_un
Apr 6 at 18:38
1
$begingroup$
@georg_un I have updated the question.
$endgroup$
– blueWings
Apr 6 at 19:11
$begingroup$
@JahKnows I have tried SVM with RBF kernel. But still, I am getting the same sensitivity and specificity. Also, I didn't get intuition behind weighting the loss by 1's
$endgroup$
– blueWings
Apr 6 at 19:30
add a comment |
$begingroup$
Can you share some instances of the data? Perhaps there are more appropriate models than a logistic regression. Also you can weight the loss caused by 1's more heavily.
$endgroup$
– JahKnows
Apr 6 at 18:03
$begingroup$
The Google Drive link will sooner or later be dead. Keep in mind that your question may be useful for somebody in the future. So, could you please add some sample lines of your data and the relevant code snippets to your question.
$endgroup$
– georg_un
Apr 6 at 18:38
1
$begingroup$
@georg_un I have updated the question.
$endgroup$
– blueWings
Apr 6 at 19:11
$begingroup$
@JahKnows I have tried SVM with RBF kernel. But still, I am getting the same sensitivity and specificity. Also, I didn't get intuition behind weighting the loss by 1's
$endgroup$
– blueWings
Apr 6 at 19:30
$begingroup$
Can you share some instances of the data? Perhaps there are more appropriate models than a logistic regression. Also you can weight the loss caused by 1's more heavily.
$endgroup$
– JahKnows
Apr 6 at 18:03
$begingroup$
Can you share some instances of the data? Perhaps there are more appropriate models than a logistic regression. Also you can weight the loss caused by 1's more heavily.
$endgroup$
– JahKnows
Apr 6 at 18:03
$begingroup$
The Google Drive link will sooner or later be dead. Keep in mind that your question may be useful for somebody in the future. So, could you please add some sample lines of your data and the relevant code snippets to your question.
$endgroup$
– georg_un
Apr 6 at 18:38
$begingroup$
The Google Drive link will sooner or later be dead. Keep in mind that your question may be useful for somebody in the future. So, could you please add some sample lines of your data and the relevant code snippets to your question.
$endgroup$
– georg_un
Apr 6 at 18:38
1
1
$begingroup$
@georg_un I have updated the question.
$endgroup$
– blueWings
Apr 6 at 19:11
$begingroup$
@georg_un I have updated the question.
$endgroup$
– blueWings
Apr 6 at 19:11
$begingroup$
@JahKnows I have tried SVM with RBF kernel. But still, I am getting the same sensitivity and specificity. Also, I didn't get intuition behind weighting the loss by 1's
$endgroup$
– blueWings
Apr 6 at 19:30
$begingroup$
@JahKnows I have tried SVM with RBF kernel. But still, I am getting the same sensitivity and specificity. Also, I didn't get intuition behind weighting the loss by 1's
$endgroup$
– blueWings
Apr 6 at 19:30
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
Actually, what is happening is natural. There is a trade-off between sensitivity and specificity. If you want to increase the specificity, you should increase the threshold of your decision function but note that it comes at a price and the price is reducing the sensitivity.
answered Apr 6 at 19:06
pythinkerpythinker
8541214
$endgroup$
$begingroup$
I see. Thank you
$endgroup$
– blueWings
Apr 6 at 19:47
$begingroup$
@blueWings You’re welcome. So, have you tried changing the threshold?
$endgroup$
– pythinker
Apr 6 at 19:49
$begingroup$
Yes. I set the threshold to 0.7 and now my specificity increased to 57%. But as you said it came with less sensitivity that is 82%. ROC remained same 0.70.
$endgroup$
– blueWings
Apr 6 at 19:59
add a comment |
$begingroup$
Just an idea. Have you tried 'playing' with C?
C is the inverse of regularization strength. Large values of C give more freedom to the model. Default C is 1.
A high C like 1000 can (not always) give you a higher variance and lower bias while you might overfit though.
Good luck!
answered Apr 7 at 9:33
FrancoSwissFrancoSwiss
10115
$endgroup$
$begingroup$
I haven't. Thank you for useful insights.
$endgroup$
– blueWings
Apr 7 at 14:50
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Actually, what is happening is natural. There is a trade-off between sensitivity and specificity. If you want to increase the specificity, you should increase the threshold of your decision function but note that it comes at a price and the price is reducing the sensitivity.
answered Apr 6 at 19:06
pythinkerpythinker
8541214
$endgroup$
$begingroup$
I see. Thank you
$endgroup$
– blueWings
Apr 6 at 19:47
$begingroup$
@blueWings You’re welcome. So, have you tried changing the threshold?
$endgroup$
– pythinker
Apr 6 at 19:49
$begingroup$
Yes. I set the threshold to 0.7 and now my specificity increased to 57%. But as you said it came with less sensitivity that is 82%. ROC remained same 0.70.
$endgroup$
– blueWings
Apr 6 at 19:59
add a comment |
$begingroup$
Actually, what is happening is natural. There is a trade-off between sensitivity and specificity. If you want to increase the specificity, you should increase the threshold of your decision function but note that it comes at a price and the price is reducing the sensitivity.
answered Apr 6 at 19:06
pythinkerpythinker
8541214
$endgroup$
$begingroup$
I see. Thank you
$endgroup$
– blueWings
Apr 6 at 19:47
$begingroup$
@blueWings You’re welcome. So, have you tried changing the threshold?
$endgroup$
– pythinker
Apr 6 at 19:49
$begingroup$
Yes. I set the threshold to 0.7 and now my specificity increased to 57%. But as you said it came with less sensitivity that is 82%. ROC remained same 0.70.
$endgroup$
– blueWings
Apr 6 at 19:59
add a comment |
$begingroup$
Actually, what is happening is natural. There is a trade-off between sensitivity and specificity. If you want to increase the specificity, you should increase the threshold of your decision function but note that it comes at a price and the price is reducing the sensitivity.
answered Apr 6 at 19:06
pythinkerpythinker
8541214
$endgroup$
Actually, what is happening is natural. There is a trade-off between sensitivity and specificity. If you want to increase the specificity, you should increase the threshold of your decision function but note that it comes at a price and the price is reducing the sensitivity.
answered Apr 6 at 19:06
pythinkerpythinker
8541214
answered Apr 6 at 19:06
pythinkerpythinker
8541214
answered Apr 6 at 19:06
pythinkerpythinker
8541214
answered Apr 6 at 19:06
pythinkerpythinker
8541214
8541214
$begingroup$
I see. Thank you
$endgroup$
– blueWings
Apr 6 at 19:47
$begingroup$
@blueWings You’re welcome. So, have you tried changing the threshold?
$endgroup$
– pythinker
Apr 6 at 19:49
$begingroup$
Yes. I set the threshold to 0.7 and now my specificity increased to 57%. But as you said it came with less sensitivity that is 82%. ROC remained same 0.70.
$endgroup$
– blueWings
Apr 6 at 19:59
add a comment |
$begingroup$
I see. Thank you
$endgroup$
– blueWings
Apr 6 at 19:47
$begingroup$
@blueWings You’re welcome. So, have you tried changing the threshold?
$endgroup$
– pythinker
Apr 6 at 19:49
$begingroup$
Yes. I set the threshold to 0.7 and now my specificity increased to 57%. But as you said it came with less sensitivity that is 82%. ROC remained same 0.70.
$endgroup$
– blueWings
Apr 6 at 19:59
$begingroup$
I see. Thank you
$endgroup$
– blueWings
Apr 6 at 19:47
$begingroup$
I see. Thank you
$endgroup$
– blueWings
Apr 6 at 19:47
$begingroup$
@blueWings You’re welcome. So, have you tried changing the threshold?
$endgroup$
– pythinker
Apr 6 at 19:49
$begingroup$
@blueWings You’re welcome. So, have you tried changing the threshold?
$endgroup$
– pythinker
Apr 6 at 19:49
$begingroup$
Yes. I set the threshold to 0.7 and now my specificity increased to 57%. But as you said it came with less sensitivity that is 82%. ROC remained same 0.70.
$endgroup$
– blueWings
Apr 6 at 19:59
$begingroup$
Yes. I set the threshold to 0.7 and now my specificity increased to 57%. But as you said it came with less sensitivity that is 82%. ROC remained same 0.70.
$endgroup$
– blueWings
Apr 6 at 19:59
add a comment |
$begingroup$
Just an idea. Have you tried 'playing' with C?
C is the inverse of regularization strength. Large values of C give more freedom to the model. Default C is 1.
A high C like 1000 can (not always) give you a higher variance and lower bias while you might overfit though.
Good luck!
answered Apr 7 at 9:33
FrancoSwissFrancoSwiss
10115
$endgroup$
$begingroup$
I haven't. Thank you for useful insights.
$endgroup$
– blueWings
Apr 7 at 14:50
add a comment |
$begingroup$
Just an idea. Have you tried 'playing' with C?
C is the inverse of regularization strength. Large values of C give more freedom to the model. Default C is 1.
A high C like 1000 can (not always) give you a higher variance and lower bias while you might overfit though.
Good luck!
answered Apr 7 at 9:33
FrancoSwissFrancoSwiss
10115
$endgroup$
$begingroup$
I haven't. Thank you for useful insights.
$endgroup$
– blueWings
Apr 7 at 14:50
add a comment |
$begingroup$
Just an idea. Have you tried 'playing' with C?
C is the inverse of regularization strength. Large values of C give more freedom to the model. Default C is 1.
A high C like 1000 can (not always) give you a higher variance and lower bias while you might overfit though.
Good luck!
answered Apr 7 at 9:33
FrancoSwissFrancoSwiss
10115
$endgroup$
Just an idea. Have you tried 'playing' with C?
C is the inverse of regularization strength. Large values of C give more freedom to the model. Default C is 1.
A high C like 1000 can (not always) give you a higher variance and lower bias while you might overfit though.
Good luck!
answered Apr 7 at 9:33
FrancoSwissFrancoSwiss
10115
answered Apr 7 at 9:33
FrancoSwissFrancoSwiss
10115
answered Apr 7 at 9:33
FrancoSwissFrancoSwiss
10115
answered Apr 7 at 9:33
FrancoSwissFrancoSwiss
10115
10115
$begingroup$
I haven't. Thank you for useful insights.
$endgroup$
– blueWings
Apr 7 at 14:50
add a comment |
$begingroup$
I haven't. Thank you for useful insights.
$endgroup$
– blueWings
Apr 7 at 14:50
$begingroup$
I haven't. Thank you for useful insights.
$endgroup$
– blueWings
Apr 7 at 14:50
$begingroup$
I haven't. Thank you for useful insights.
$endgroup$
– blueWings
Apr 7 at 14:50
add a comment |
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$begingroup$
Can you share some instances of the data? Perhaps there are more appropriate models than a logistic regression. Also you can weight the loss caused by 1's more heavily.
$endgroup$
– JahKnows
Apr 6 at 18:03
$begingroup$
The Google Drive link will sooner or later be dead. Keep in mind that your question may be useful for somebody in the future. So, could you please add some sample lines of your data and the relevant code snippets to your question.
$endgroup$
– georg_un
Apr 6 at 18:38
1
$begingroup$
@georg_un I have updated the question.
$endgroup$
– blueWings
Apr 6 at 19:11
$begingroup$
@JahKnows I have tried SVM with RBF kernel. But still, I am getting the same sensitivity and specificity. Also, I didn't get intuition behind weighting the loss by 1's
$endgroup$
– blueWings
Apr 6 at 19:30