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One attribute includes another attribute
Pre-processing (center, scale, impute) among training sets (different forms) and the test set - what is a good approach?What is the best way to scale a numerical datasetSelecting the number of hashes for minhash? Working with extremely sparse data and want more collisionsPython: Handling imbalance Classes in python Machine LearningChoice of replacing missing values based on the data distributionNested features with one to many relationshipsHow to use a dataset where attribute names are changed?Dealing with a dataset where a subset of points live in a higher dimensional spaceDifferent approaches of creating the test setHow to deal with attributes that can vary arbitrarily for each sample?
$begingroup$
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 Apr 8 at 16:52
AymanAyman
1
$endgroup$
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 Apr 8 at 16:52
AymanAyman
1
$endgroup$
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 Apr 8 at 16:52
AymanAyman
1
$endgroup$
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 Apr 8 at 16:52
AymanAyman
1
asked Apr 8 at 16:52
AymanAyman
1
asked Apr 8 at 16:52
AymanAyman
1
asked Apr 8 at 16:52
AymanAyman
1
asked Apr 8 at 16:52
AymanAyman
1
1
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
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 Apr 8 at 17:11
Mina NaghshnejadMina Naghshnejad
1
$endgroup$
$begingroup$
I already applied PCA but it didn't distinguish between both, also it gave plan high principal
$endgroup$
– Ayman
Apr 11 at 16:21
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
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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 Apr 8 at 17:11
Mina NaghshnejadMina Naghshnejad
1
$endgroup$
$begingroup$
I already applied PCA but it didn't distinguish between both, also it gave plan high principal
$endgroup$
– Ayman
Apr 11 at 16:21
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 Apr 8 at 17:11
Mina NaghshnejadMina Naghshnejad
1
$endgroup$
$begingroup$
I already applied PCA but it didn't distinguish between both, also it gave plan high principal
$endgroup$
– Ayman
Apr 11 at 16:21
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 Apr 8 at 17:11
Mina NaghshnejadMina Naghshnejad
1
$endgroup$
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 Apr 8 at 17:11
Mina NaghshnejadMina Naghshnejad
1
answered Apr 8 at 17:11
Mina NaghshnejadMina Naghshnejad
1
answered Apr 8 at 17:11
Mina NaghshnejadMina Naghshnejad
1
answered Apr 8 at 17:11
Mina NaghshnejadMina Naghshnejad
1
1
$begingroup$
I already applied PCA but it didn't distinguish between both, also it gave plan high principal
$endgroup$
– Ayman
Apr 11 at 16:21
add a comment |
$begingroup$
I already applied PCA but it didn't distinguish between both, also it gave plan high principal
$endgroup$
– Ayman
Apr 11 at 16:21
$begingroup$
I already applied PCA but it didn't distinguish between both, also it gave plan high principal
$endgroup$
– Ayman
Apr 11 at 16:21
$begingroup$
I already applied PCA but it didn't distinguish between both, also it gave plan high principal
$endgroup$
– Ayman
Apr 11 at 16:21
add a comment |
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