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Can you pass categorical data to sklearn decision tree?
As it stands, sklearn decision trees do not handle categorical data – see issue #5442. The recommended approach of using Label Encoding converts to integers which the DecisionTreeClassifier () will treat as numeric. If your categorical data is not ordinal, this is not good – you’ll end up with splits that do not make sense.
Which is better, a k or a decision tree?
Since KNN performs instance-based learning, a well-tuned K can model complex decision spaces having arbitrarily complicated decision boundaries, which are not easily modeled by other “eager” learners like Decision Trees. “Eager” learners work in batches, modeling one group of training observations at a time.
How are categorical features and encoding in decision trees?
One hot encoding requires.. as many splits as there are categories, which means a crazy lot and so much it stops very quickly because if you do one split, one part of the split will be frozen (because it is a perfect rule dataset) => it gives this escalator-like shape, thus very poorly performing.
What’s the difference between a k-NN and a KNN?
Both, k-NN and decision trees are supervised algorithms (unlike mentioned in one of the answers). They both require labelled training data in order to label the test data. k-D trees are a neat way of optimizing the k-NN algorithm. They reject large sections of the data so that classification doesn’t take too long.
How to know if my decision tree model is good or bad?
To check whether your model is good or bad, you would have to plot your losses against parameters for both train and cross-validation data set. Parameter values, where differences among the losses of train and cv data are very close & also minimal, implies a good model with those parameter values.
How to convert categorical strings to numbers in sklearn?
Sklearn Decision Trees do not handle conversion of categorical strings to numbers. I suggest you find a function in Sklearn (maybe this) that does so or manually write some code like:
When to use label encoding in decisiontreeclassifier?
The recommended approach of using Label Encoding converts to integers which the DecisionTreeClassifier () will treat as numeric. If your categorical data is not ordinal, this is not good – you’ll end up with splits that do not make sense.