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fixing errors in documentatio

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Stuart Lynn 2016-06-28 18:03:52 +00:00
parent 5f98735ce9
commit 068c80f369
1 changed files with 6 additions and 5 deletions
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11
doc/12_segmentation.md
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@ -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