Answer :
Yes, it is true that it is possible to categorize data in infinite-dimension data using random forests. Since we are dealing with subsets of data, random forest works well with high-dimensional data.
When using Random Forests, there is virtually no risk in preserving columns whose value is uncertain as well as little harm in adding new columns.
The random forests or random decision forests ensemble learning strategy, which is used for classification, regression, and other tasks, build a lot of decision trees during the training phase. A random forest is made up of several Decision Trees that each independently predict something. To determine the final result, the values are averaged (Regression) or max-voted (Classification).
This model's strength is in its ability to construct several trees from the features with various sub-features. Because each tree's features are chosen at random, the trees do not grow very deep and just concentrate on the set of features.
Finally, after combining them, we get an ensemble of Decision Trees that offers a forecast that has been learned.
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