Improve this question. Nick Stauner Add a comment. Active Oldest Votes. Improve this answer. Nick Cox Nick Cox To convince yourself, try it both ways and see that nothing important changes.
Thus it's a good idea to keep the binary indicator expressed in a meaningful way. Standardizing it doesn't sound either good or useful. You need to examine how your software treats the data in order to decide whether prior standardization make sense.
Show 10 more comments. As I said, they mean stuff like "recognized a face" and "not recognized face", and means the confidence level.
Does it make sense to z-score that? Search on the ordinal tag to learn more. Is this a numerical stability issue? Collectives on Stack Overflow. Learn more. Dummy variables, is necessary to standardize them? Ask Question. Asked 3 years, 5 months ago. Active 3 years, 1 month ago.
Viewed 7k times. I wanted to narrate this process, to arrive at my question. Improve this question. In this post question I've received the orientation about it.
There is a difference between when use LabelEncoder and when use OneHotEncoder , in my question above I am using togethers and I get the expected result that is the codification with LabelEncoder and categorization with OneHotEncoder treat them these values like a categorical values avoidig the weight inconvenient in relation to these values.
But, there is the pd. The other prospective depends on the algorithm you choose. As these algorithms work using ensembling method with conditions during each split.
At the same time if you are using a Linear Algorithm then One hot encoding will perform better compared to label encoding as the weights are shifted towards higher numbered label 0 vs 7 as PulkitS explains. For the second question, this really depends on the dataset and other features. In my implementation, scaling worked when I transformed my target variable with log1p conversion.
PulkitS and Shaz13 In the first instance, thanks a lot for you for the orientations. According to what you tell me, is necessary to analyze when use LabelEncoder or OneHotEncoder , but not use together? LabelEncoder simply encode the values into number according to how many categories I have. But as this solution brings the numerical values weight problem in my case 0 to 7 and my direction of the wind values does not have any order. Or does not matter? I bring my apologies to you PulkitS and Shaz13 if my questions and considerations are newbies, but I am a bit confused about here.
Best regards and many thanks for your time and effort. Now I see your concern. For example, we are performing customer segmentation analysis in which we are trying to group customers based on their homogenous similar attributes. Hence, it is required to transform the data to comparable scales. They are as follows - 1. Z score. Check Mean and Variance of Standardized Variable. A value of 1 implies that the value for that case is one standard deviation above the mean, while a value of -1 indicates that a case has a value one standard deviations lower than the mean.
Standardization after missing imputation and outlier treatment. Standardization and Tree Algorithms and Logistic Regression. Statistics Tutorials : 50 Statistics Tutorials.
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