Decision Tree

Setting Up a Decision Tree Classifier
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.classification import DecisionTreeClassifier
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
from pyspark.ml.evaluation import BinaryClassificationEvaluator
Initialize Decision Tree object
dt = DecisionTreeClassifier(labelCol="label", featuresCol="features")
Create a parameter grid for tuning the model
dtparamGrid = (ParamGridBuilder()
             .addGrid(dt.maxDepth, [2, 5, 10])
             .addGrid(dt.maxBins, [10, 20])
             .build())
Define how you want the model to be evaluated
dtevaluator = BinaryClassificationEvaluator(rawPredictionCol="rawPrediction")
Define the type of cross-validation you want to perform
# Create 5-fold CrossValidator
dtcv = CrossValidator(estimator = dt,
                      estimatorParamMaps = dtparamGrid,
                      evaluator = dtevaluator,
                      numFolds = 5)
Fit the model to the data
dtcvModel = dtcv.fit(train)
print(dtcvModel)
Score the testing dataset using your fitted model for evaluation purposes
dtpredictions = dtcvModel.transform(test)
Evaluate the model
print('Accuracy:', dtevaluator.evaluate(dtpredictions))
print('AUC:', BinaryClassificationMetrics(dtpredictions['label','prediction'].rdd).areaUnderROC)
print('PR:', BinaryClassificationMetrics(dtpredictions['label','prediction'].rdd).areaUnderPR)
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.
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Last updated 9 months ago