RandomForestRegressor leads to KeyError: ‘squared_error’
I am trying to predict house prices in the Cali housing data set with a random forest. I do not understand why I get a KeyError: 'squared_error' in this simple code:
random-forest
How to extract the decision rules from scikit-learn decision-tree?
Can I extract the underlying decision-rules (or ‘decision paths’) from a trained tree in a decision tree as a textual list?
Predict classes or class probabilities?
I am currently using H2O for a classification problem dataset. I am testing it out with H2ORandomForestEstimator in a python 3.6 environment. I noticed the results of the predict method was giving values between 0 to 1(I am assuming this is the probability).
Why is Random Forest with a single tree much better than a Decision Tree classifier?
I apply the
decision tree classifier and the random forest classifier to my data with the following code: