Contents
Can random forest handle string?
1) How to add “strings” as features. Very few algorithms can natively handle strings in any form, and decision trees are not one of them. You have to convert them to something that the decision tree knows about (generally numeric or categorical variables).
What are features in random forest?
Random forest feature importance. Random forests are among the most popular machine learning methods thanks to their relatively good accuracy, robustness and ease of use. They also provide two straightforward methods for feature selection: mean decrease impurity and mean decrease accuracy.
Can you use strings as features in a decision tree?
Very few algorithms can natively handle strings in any form, and decision trees are not one of them. You have to convert them to something that the decision tree knows about (generally numeric or categorical variables). How to convert them to features: This very much depends on the nature of the strings.
How is a random forest different from a decision tree?
If we use same or very similar trees, overall result will not be much different than the result of a single decision tree. Random forests achieve to have uncorrelated decision trees by bootstrapping and feature randomness. Bootsrapping is randomly selecting samples from training data with replacement.
How to control the number of features in a random forest?
The number of features used for each tree in a random forest can be controlled with max_features parameter. Bootstrap samples and feature randomness provide the random forest model with uncorrelated trees.
How are factors used in scikit-learn decision trees?
For example in R you would use factors, in WEKA you would use nominal variables. This is not the case in scikit-learn. The decision trees implemented in scikit-learn uses only numerical features and these features are interpreted always as continuous numeric variables.