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Target encoding calculated using an appropriate cross-validation strategy can also be powerful for high-cardinality categorical features. In some instances, frequency encoding can also be useful.


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You could use Binary or BaseN encoding because they are commonly used with high dimensional nominal categories. Because binary or baseN encoding encodes the categories into ordinal numbers and then into binary or base of N respectively in an effective way. They are so efficient in terms of complexity and the dimensionality. I recommend you to read this for ...


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So, Clustering is "Unsupervised" learning : You make groups in which elements look like each-other. In Unsupervised learning, you don't have a Label that you look for. Here, your problem is to Classify text between 3 categories : Sports, Foreign, Local. Those 3 categories ARE labels : You know you have news about those 3 subjects, and want to make ...


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The standard method to encode a categorical variable is one hot encoding. Replacing categories with numbers (ordinal encoding) would certainly introduce errors in the model because it would rely on numerical comparisons which are meaningless with categorical values. The high number of dimensions can be a problem if the number of instances is too low and/or ...


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First let me answer your specific question: If you want to decide which feature of two highly correlated, high impact features I would look at the following additional attributes of your features: How is the data quality or amount of data? Is one better or higher than the other? Choose this one. Is it in any way harmful to remove one of the features? If yes,...


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