One attribute includes another attribute
$begingroup$
I have a telecom dataset that has many attributes, among these attributes, there is "Voice mail plan" attribute that takes yes or no, and another attribute is "voice mail calls" which has many values, but always zero when "Voice mail plan" is no. When removing "Voice mail plan" from the dataset the accuracy of classifiers is lowered, so how can we inform the classifier that No is impeded in zero voice calls
data-mining preprocessing
asked 10 hours ago
AymanAyman
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I have a telecom dataset that has many attributes, among these attributes, there is "Voice mail plan" attribute that takes yes or no, and another attribute is "voice mail calls" which has many values, but always zero when "Voice mail plan" is no. When removing "Voice mail plan" from the dataset the accuracy of classifiers is lowered, so how can we inform the classifier that No is impeded in zero voice calls
data-mining preprocessing
asked 10 hours ago
AymanAyman
1
New contributor
Ayman 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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add a comment |
$begingroup$
I have a telecom dataset that has many attributes, among these attributes, there is "Voice mail plan" attribute that takes yes or no, and another attribute is "voice mail calls" which has many values, but always zero when "Voice mail plan" is no. When removing "Voice mail plan" from the dataset the accuracy of classifiers is lowered, so how can we inform the classifier that No is impeded in zero voice calls
data-mining preprocessing
asked 10 hours ago
AymanAyman
1
New contributor
Ayman is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a telecom dataset that has many attributes, among these attributes, there is "Voice mail plan" attribute that takes yes or no, and another attribute is "voice mail calls" which has many values, but always zero when "Voice mail plan" is no. When removing "Voice mail plan" from the dataset the accuracy of classifiers is lowered, so how can we inform the classifier that No is impeded in zero voice calls
data-mining preprocessing
data-mining preprocessing
asked 10 hours ago
AymanAyman
1
New contributor
Ayman is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 hours ago
AymanAyman
1
New contributor
Ayman is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 hours ago
AymanAyman
1
New contributor
Ayman is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 hours ago
AymanAyman
1
asked 10 hours ago
AymanAyman
1
1
New contributor
Ayman is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Ayman 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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add a comment |
1 Answer
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The two features "voice mail calls" and "voice mail plan" are related but they are not linearly correlated. "Voice mail plan" still contains some information that is not available from other features. Why do you want to remove "Voice mail plan" in first place? If you need to decrease your number of features, you can try dimension reduction (linear or non-linear), this way you make sure most of the variance of your feature set is considered for building your model.
answered 10 hours ago
Mina NaghshnejadMina Naghshnejad
1
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PCA treated VM plan as high principal
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– Ayman
7 hours ago
add a comment |
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1 Answer
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1 Answer
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$begingroup$
The two features "voice mail calls" and "voice mail plan" are related but they are not linearly correlated. "Voice mail plan" still contains some information that is not available from other features. Why do you want to remove "Voice mail plan" in first place? If you need to decrease your number of features, you can try dimension reduction (linear or non-linear), this way you make sure most of the variance of your feature set is considered for building your model.
answered 10 hours ago
Mina NaghshnejadMina Naghshnejad
1
New contributor
Mina Naghshnejad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
PCA treated VM plan as high principal
$endgroup$
– Ayman
7 hours ago
add a comment |
$begingroup$
The two features "voice mail calls" and "voice mail plan" are related but they are not linearly correlated. "Voice mail plan" still contains some information that is not available from other features. Why do you want to remove "Voice mail plan" in first place? If you need to decrease your number of features, you can try dimension reduction (linear or non-linear), this way you make sure most of the variance of your feature set is considered for building your model.
answered 10 hours ago
Mina NaghshnejadMina Naghshnejad
1
New contributor
Mina Naghshnejad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
PCA treated VM plan as high principal
$endgroup$
– Ayman
7 hours ago
add a comment |
$begingroup$
The two features "voice mail calls" and "voice mail plan" are related but they are not linearly correlated. "Voice mail plan" still contains some information that is not available from other features. Why do you want to remove "Voice mail plan" in first place? If you need to decrease your number of features, you can try dimension reduction (linear or non-linear), this way you make sure most of the variance of your feature set is considered for building your model.
answered 10 hours ago
Mina NaghshnejadMina Naghshnejad
1
New contributor
Mina Naghshnejad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
The two features "voice mail calls" and "voice mail plan" are related but they are not linearly correlated. "Voice mail plan" still contains some information that is not available from other features. Why do you want to remove "Voice mail plan" in first place? If you need to decrease your number of features, you can try dimension reduction (linear or non-linear), this way you make sure most of the variance of your feature set is considered for building your model.
answered 10 hours ago
Mina NaghshnejadMina Naghshnejad
1
New contributor
Mina Naghshnejad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 10 hours ago
Mina NaghshnejadMina Naghshnejad
1
New contributor
Mina Naghshnejad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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answered 10 hours ago
Mina NaghshnejadMina Naghshnejad
1
answered 10 hours ago
Mina NaghshnejadMina Naghshnejad
1
1
New contributor
Mina Naghshnejad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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New contributor
Mina Naghshnejad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Mina Naghshnejad is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
PCA treated VM plan as high principal
$endgroup$
– Ayman
7 hours ago
add a comment |
$begingroup$
PCA treated VM plan as high principal
$endgroup$
– Ayman
7 hours ago
$begingroup$
PCA treated VM plan as high principal
$endgroup$
– Ayman
7 hours ago
$begingroup$
PCA treated VM plan as high principal
$endgroup$
– Ayman
7 hours ago
add a comment |
Ayman is a new contributor. Be nice, and check out our Code of Conduct.
Ayman is a new contributor. Be nice, and check out our Code of Conduct.
Ayman is a new contributor. Be nice, and check out our Code of Conduct.
Ayman is a new contributor. Be nice, and check out our Code of Conduct.
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