混合数据集中的特征选择

Question

I am a newbie in data science domain.

I have a data set, which has both numerical and string data.The interesting fact is both type of data make sense for the outcome. How to choose the relevant features from the data set?

Should I be using the LabelEncoder and convert the data from string to numerical and continue with the correlation? I am taking the right path? Is there any better way to solve this crisis?

Answer 1

Kind of a cop out but you could simply use a random forest and happily mix numerical and categorical data. Encoding with LabelEncoder on OneHotEncoding would allow you to use a wider variety of algorithms.

Answer 2

You can encode categorical variables with label encoding if there is a meaningful ordering of available values and making sure the ordering is retained in the encoding. See here for an example.

If there's no ordering (or resolving a meaningful one is too much work) you can use one-hot encoding. This, however will increase the feature set proportionally to the distinct values for the feature in the dataset.

If one-hot results in a very large feature set and the categorical string data are natural language words, you may want to use a pretrained embedding.

Either way, you can then concatenate the encoded categorical column(s) to the continuous feature set and proceed with learning and feature selection.