diff --git a/doc/12_segmentation.md b/doc/12_segmentation.md index 2404730..b6b0c95 100644 --- a/doc/12_segmentation.md +++ b/doc/12_segmentation.md @@ -50,6 +50,12 @@ This function trains a [Gradient Boosting](http://scikit-learn.org/stable/module | train\_features| numeric[] | 1D array of length n features \* n\_rows + 1 with the first entry in the array being the number of features in each row. These are the features the model will be trained on. CDB\_Crankshaft.CDB_pyAgg(Array[feature1, feature2, feature3]::numeric[]) can be used to construct this. | | prediction\_features | numeric[] | 1D array of length nfeatures\* n\_rows\_ + 1 with the first entry in the array being the number of features in each row. These are the features that will be used to predict the target variable CDB\_Crankshaft.CDB\_pyAgg(Array[feature1, feature2, feature3]::numeric[]) can be used to construct this. | | prediction\_ids | numeric[] | 1D array of length n\_rows with the ids that can use used to re-join the data with inputs | +| n\_estimators (optional) | INTEGER DEFAULT 1200 | Number of estimators to be used | +| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth | +| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample parameter for GradientBooster| +| learning\_rate (optional) | DOUBLE PRECISION DEFAULT 0.01 | Learning rate for the GradientBooster | +| min\_samples\_leaf (optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf | + #### Returns @@ -60,11 +66,6 @@ A table with the following columns. | cartodb\_id | INTEGER | The CartoDB id of the row in the target\_query | | prediction | NUMERIC | The predicted value of the variable of interest | | accuracy | NUMERIC | The mean squared accuracy of the model. | -| n\_estimators (optional) | INTEGER DEFAULT 1200 | Number of estimators to be used | -| max\_depth (optional) | INTEGER DEFAULT 3 | Max tree depth | -| subsample (optional) | DOUBLE PRECISION DEFAULT 0.5 | Subsample parameter for GradientBooster| -| learning\_rate (optional) | DOUBLE PRECISION DEFAULT 0.01 | Learning rate for the GradientBooster | -| min\_samples\_leaf (optional) | INTEGER DEFAULT 1 | Minimum samples to use per leaf | #### Example Usage