Boosting is a method for combining a series of simple individual models to create a more powerful model. The name **gradient boosting** arises because target outcomes are set based on the gradient of the error with respect to the prediction of each case. Each new model takes a step in the direction that minimizes prediction error in the space of predictions for each training case.

This article describes how to create a Gradient boosting Importance output as shown below.

## Requirements

- A numeric or categorical variable to be used as an
**Outcome**variable to be predicted. **Predictors**variables will be considered as predictors of the outcome variable.

## Method

- In the
**Anything**menu select**Advanced Analysis >****Machine Learning > Gradient Boosting.** - In the
**object inspector**go to the**Inputs**tab. - In the
**Output**menu select the variable to be predicted by the*predictor variables.* - Select the predictor variable(s) from the
**Predictor(s)**list. - OPTIONAL: Select the desired
**Output**type:**Accuracy**: Produces measures of the goodness of model fit. For categorical*outcomes*the breakdown by category is shown.**Importance**: As shown above. It produces a chart showing the importance of the*predictors*in determining the*outcome*. Only available for*gbtree*booster.**Prediction-Accuracy Table**: Produces a table relating the observed and predicted*outcome*. Also known as a confusion matrix.**Detail**Text output from the underlying*xgboost*package.

- OPTIONAL: Select the desired
**Missing Data**treatment. (See Missing Data Options). - OPTIONAL: Select
**Variable names**to display variable names in the output instead of labels. - OPTIONAL: Select the underlying
**Booster**model. Chose between**gbtree**and**gblinear**. - OPTIONAL: Enable
**Grid search**to search the parameter space in order to tune the model. If not checked, the default parameters of`xgboost`are used. Increasing this will usually create a more accurate predictor, at the cost of taking longer to run.

## See Also

How to Create a Classification And Regression Trees (CART)

How to Run Machine Learning Diagnostics - Prediction-Accuracy Table

How to Run Machine Learning Diagnostics - Table of Discriminant Function Coefficients extension

How to Create an Ensemble of Machine Learning Models

How to Compare Machine Learning Models

How to Run Machine Learning Linear Discriminant Analysis

How to Save Machine Learning Discrimination Variables

How to Save Machine Learning Predicted Values Variables

How to Save Machine Learning Probability of Each Response Variable

How to Run Support Vector Machine

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