Pros and cons of using the zscore of a dataset before normalizing it during feature engineering? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsHow to perform feature engineering on unknown features?Automatic Feature EngineeringHow To Merge Features in the Dataset Forest Cover Type Classification Problem?Is feature engineering still useful when using XGBoost?Feature engineering on distributionsFix missing data by adding another feature instead of using the mean?Feature engineering for hierarchical dataHow to do feature engineering for email cleaning / text extraction?Manual feature engineering based on the outputMachine learning algorithm that can use many instances to predict 1 continuous outcome per person
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Pros and cons of using the zscore of a dataset before normalizing it during feature engineering?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsHow to perform feature engineering on unknown features?Automatic Feature EngineeringHow To Merge Features in the Dataset Forest Cover Type Classification Problem?Is feature engineering still useful when using XGBoost?Feature engineering on distributionsFix missing data by adding another feature instead of using the mean?Feature engineering for hierarchical dataHow to do feature engineering for email cleaning / text extraction?Manual feature engineering based on the outputMachine learning algorithm that can use many instances to predict 1 continuous outcome per person
$begingroup$
Normalization is a common feature engineering technique. However, this post used standardize(zscore) on the dataset before normalizing it.
I think that would result in losing some of the information in data.
What are the pros and cons of doing this?
machine-learning feature-engineering
edited Apr 4 at 3:40
Ethan
706625
asked Apr 4 at 2:38
fu DLfu DL
84
$endgroup$
add a comment |
$begingroup$
Normalization is a common feature engineering technique. However, this post used standardize(zscore) on the dataset before normalizing it.
I think that would result in losing some of the information in data.
What are the pros and cons of doing this?
machine-learning feature-engineering
edited Apr 4 at 3:40
Ethan
706625
asked Apr 4 at 2:38
fu DLfu DL
84
$endgroup$
add a comment |
$begingroup$
Normalization is a common feature engineering technique. However, this post used standardize(zscore) on the dataset before normalizing it.
I think that would result in losing some of the information in data.
What are the pros and cons of doing this?
machine-learning feature-engineering
edited Apr 4 at 3:40
Ethan
706625
asked Apr 4 at 2:38
fu DLfu DL
84
$endgroup$
Normalization is a common feature engineering technique. However, this post used standardize(zscore) on the dataset before normalizing it.
I think that would result in losing some of the information in data.
What are the pros and cons of doing this?
machine-learning feature-engineering
machine-learning feature-engineering
edited Apr 4 at 3:40
Ethan
706625
asked Apr 4 at 2:38
fu DLfu DL
84
edited Apr 4 at 3:40
Ethan
706625
asked Apr 4 at 2:38
fu DLfu DL
84
edited Apr 4 at 3:40
Ethan
706625
edited Apr 4 at 3:40
Ethan
706625
edited Apr 4 at 3:40
Ethan
706625
706625
asked Apr 4 at 2:38
fu DLfu DL
84
asked Apr 4 at 2:38
fu DLfu DL
84
asked Apr 4 at 2:38
fu DLfu DL
84
84
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
Z-scores normalisation are a way to compare results from a test to a “normal” population and bring them to a same comparable scale. Advantages of ZScore can thus be:
$$ z_score = fracx-bar xsigma $$
The Z score normalisation has the following advantages:
- Z Score can be used to compare raw scores that are taken from different tests
- Z score takes into account both the mean value and the variability in a set of raw scores.
And the Disadvantages of Z score are:
- Z Score always assume a normal distribution.
- If the data is skewed, the distribution of the left and right of the origin line is not equal.
answered Apr 4 at 5:47
thanatozthanatoz
689421
$endgroup$
add a comment |
$begingroup$
Normalizing an already normalized dataset should not change anything unless for some reason a different normalization scheme is used.
answered Apr 4 at 7:36
seraliserali
1
$endgroup$
add a comment |
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2 Answers
2
active
oldest
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2 Answers
2
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$begingroup$
Z-scores normalisation are a way to compare results from a test to a “normal” population and bring them to a same comparable scale. Advantages of ZScore can thus be:
$$ z_score = fracx-bar xsigma $$
The Z score normalisation has the following advantages:
- Z Score can be used to compare raw scores that are taken from different tests
- Z score takes into account both the mean value and the variability in a set of raw scores.
And the Disadvantages of Z score are:
- Z Score always assume a normal distribution.
- If the data is skewed, the distribution of the left and right of the origin line is not equal.
answered Apr 4 at 5:47
thanatozthanatoz
689421
$endgroup$
add a comment |
$begingroup$
Z-scores normalisation are a way to compare results from a test to a “normal” population and bring them to a same comparable scale. Advantages of ZScore can thus be:
$$ z_score = fracx-bar xsigma $$
The Z score normalisation has the following advantages:
- Z Score can be used to compare raw scores that are taken from different tests
- Z score takes into account both the mean value and the variability in a set of raw scores.
And the Disadvantages of Z score are:
- Z Score always assume a normal distribution.
- If the data is skewed, the distribution of the left and right of the origin line is not equal.
answered Apr 4 at 5:47
thanatozthanatoz
689421
$endgroup$
add a comment |
$begingroup$
Z-scores normalisation are a way to compare results from a test to a “normal” population and bring them to a same comparable scale. Advantages of ZScore can thus be:
$$ z_score = fracx-bar xsigma $$
The Z score normalisation has the following advantages:
- Z Score can be used to compare raw scores that are taken from different tests
- Z score takes into account both the mean value and the variability in a set of raw scores.
And the Disadvantages of Z score are:
- Z Score always assume a normal distribution.
- If the data is skewed, the distribution of the left and right of the origin line is not equal.
answered Apr 4 at 5:47
thanatozthanatoz
689421
$endgroup$
Z-scores normalisation are a way to compare results from a test to a “normal” population and bring them to a same comparable scale. Advantages of ZScore can thus be:
$$ z_score = fracx-bar xsigma $$
The Z score normalisation has the following advantages:
- Z Score can be used to compare raw scores that are taken from different tests
- Z score takes into account both the mean value and the variability in a set of raw scores.
And the Disadvantages of Z score are:
- Z Score always assume a normal distribution.
- If the data is skewed, the distribution of the left and right of the origin line is not equal.
answered Apr 4 at 5:47
thanatozthanatoz
689421
answered Apr 4 at 5:47
thanatozthanatoz
689421
answered Apr 4 at 5:47
thanatozthanatoz
689421
answered Apr 4 at 5:47
thanatozthanatoz
689421
689421
add a comment |
add a comment |
$begingroup$
Normalizing an already normalized dataset should not change anything unless for some reason a different normalization scheme is used.
answered Apr 4 at 7:36
seraliserali
1
$endgroup$
add a comment |
$begingroup$
Normalizing an already normalized dataset should not change anything unless for some reason a different normalization scheme is used.
answered Apr 4 at 7:36
seraliserali
1
$endgroup$
add a comment |
$begingroup$
Normalizing an already normalized dataset should not change anything unless for some reason a different normalization scheme is used.
answered Apr 4 at 7:36
seraliserali
1
$endgroup$
Normalizing an already normalized dataset should not change anything unless for some reason a different normalization scheme is used.
answered Apr 4 at 7:36
seraliserali
1
answered Apr 4 at 7:36
seraliserali
1
answered Apr 4 at 7:36
seraliserali
1
answered Apr 4 at 7:36
seraliserali
1
1
add a comment |
add a comment |
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