Comment on page

Gradient-Boosted Trees

Setting Up Gradient-Boosted Tree Regression
Note: Make sure you have your training and test data already vectorized and ready to go before you begin trying to fit the machine learning model to unprepped data.
Load in required libraries
from pyspark.ml.regression import GBTRegressor
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
from pyspark.ml.evaluation import RegressionEvaluator
Initialize Gradient-Boosted Tree object
gb = GBTRegressor(labelCol="label", featuresCol="features")
Create a parameter grid for tuning the model
gbparamGrid = (ParamGridBuilder()
             .addGrid(gb.maxDepth, [2, 5, 10])
             .addGrid(gb.maxBins, [10, 20, 40])
             .addGrid(gb.maxIter, [5, 10, 20])
             .build())
Define how you want the model to be evaluated
gbevaluator = RegressionEvaluator(predictionCol="prediction", labelCol="label", metricName="rmse")
Define the type of cross-validation you want to perform
# Create 5-fold CrossValidator
gbcv = CrossValidator(estimator = gb,
                      estimatorParamMaps = gbparamGrid,
                      evaluator = gbevaluator,
                      numFolds = 5)
Fit the model to the data
gbcvModel = gbcv.fit(train)
print(gbcvModel)
Score the testing dataset using your fitted model for evaluation purposes
gbpredictions = gbcvModel.transform(test)
Evaluate the model
print('RMSE:', gbevaluator.evaluate(gbpredictions))
Note: When you use the CrossValidator function to set up cross-validation of your models, the resulting model object will have all the runs included, but will only use the best model when you interact with the model object using other functions like evaluate or transform.

Random Forest

Next - Machine Learning

MLflow

Last modified 4yr ago