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When does a random forest model over predict?
If you’re training data is missing any time periods, your Random Forest model will under or over predict, depending on the trend, examples outside of the time frames in your training data. This will be very noticeable if you plot your model’s predictions against their true values. Let’s take a look at this by creating some data.
Are there any values outside the training set in random forest?
There are no values outside that range. Random Forest cannot extrapolate. As you have seen above, when using a Random Forest Regressor, the predicted values are never outside the training set values for the target variable. If you look at prediction values they will look like this:
What does a random forest regressor look like?
As you have seen above, when using a Random Forest Regressor, the predicted values are never outside the training set values for the target variable. If you look at prediction values they will look like this:
Can a random forest be used to extrapolate?
A Random Forest’s nonlinear nature can give it a leg up over linear algorithms, making it a great option. However, it is important to know your data and keep in mind that a Random Forest can’t extrapolate. It can only make a prediction that is an average of previously observed labels.
How is random forest regression used in business?
Random forest regression is used to solve a variety of business problems where the company needs to predict a continuous value: Predict future prices/costs.
What causes high variance in random forest regression?
This causes high variance, which can be seen as high test errors on the test dataset, despite high accuracy on the training dataset. In other words, decision trees do not generalize well to novel data. Decision trees are easily swayed by data that splits the attributes well.
How are random forests not sensitive to outliers?
Edit. Fortunately, while the base CART/RF algorithm is emphatically not robust to outliers, it is possible (and quiet easy) to modify the procedure to impart it robustness to “y”-outliers. I will now focus on regression RF’s (since this is more specifically the object of the OP’s question).