XGBoost Regressor

Input

It takes in a DataFrame as input and performs XGBoost Regression

Output

The XGBoost Model generated is passed along to the next nodes. The input DataFrame is also passed along to the next nodes

Type

ml-estimator

Class

fire.nodes.ml.NodeXGBoostRegressor

Fields

Name	Title	Description
featuresCol	Features Column	Features column of type vectorUDT for model fitting
labelCol	Label Column	The label column for model fitting
predictionCol	Prediction Column	The prediction column created during model scoring.
splitRatio	Split Ratio	Split Ratio
maxDepth	Max Depth	The Maximum depth of a tree
maxBins	Max Bins	The maximum number of bins used for discretizing continuous features.Must be >= 2 and >= number of categories in any categorical feature.
maxLeaves	Max Leaves
numRound	Num Round
numWorkers	Num Workers
objective	Objective
eta	Eta
regLambda	Reg Lambda
regAlpha	Reg Alpha
subsample	Sub Aample
sampleType	Sample Type
treeMethod	Tree Method
useExternalMemory	Use External Memory
seed	Seed
baseScore	Base Score
minChildWeight	Min Child Weight
colsampleBylevel	Col Sample By Level
colsampleBytree	Col Sample By Tree
minSplitLoss	Min Split Loss
maxDeltaStep	Max Delta Step
sketchEps	Sketch Eps
scalePosWeight	Scale Pos Weight
growPlicy	Grow Policy
normalizeType	Normalize Type
skipDrop	Skip Drop
rateDrop	Rate Drop

Details

Deatils: https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_tutorial.html#xgboost4j-spark-tutorial-version-0-9

XGBoost Regressor Node Details

This node implements the XGBoost algorithm for regression tasks. It can be used for a variety of regression problems, including predicting continuous values, such as stock prices, house prices, or weather patterns.

Key Parameters:

Features Column: The name of the column containing the features used for training.

Label Column: The name of the column containing the target variable to be predicted.

Prediction Column: The name of the column where the predicted values will be stored.

Max Depth: The maximum depth of each tree in the ensemble. Higher values can lead to overfitting.

Max Bins: The maximum number of bins to use for histogram-based approximations.

Max Leaves: The maximum number of leaves per tree.

Num Round: The number of boosting rounds (trees) to build.

Num Workers: The number of threads to use for parallel processing.

Objective: The objective function to optimize. ‘reg:linear’ is used for linear regression.

Eta: The learning rate, which controls the step size at each boosting round.

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Examples

XGBoost Regressor Node Example

Scenario:

Let’s assume we have a dataset containing information about houses, including features like size, number of bedrooms, location, etc., and the corresponding target variable being the house price.

Configuration:

1. Features Column: “features”

2. Label Column: “price”

3. Prediction Column: “predicted_price”

4. Max Depth: 5

5. Num Round: 100

6. Eta: 0.1

7. Objective: “reg:linear”

Execution:

When this node is executed, the XGBoost algorithm will train a regression model using the specified parameters. The model will then be used to predict the house prices for new data points.

Output:

The predicted house prices will be stored in the “predicted_price” column of the output dataset.