本文整理汇总了Python中pyspark.ml.evaluation.MulticlassClassificationEvaluator方法的典型用法代码示例。如果您正苦于以下问题：Python evaluation.MulticlassClassificationEvaluator方法的具体用法？Python evaluation.MulticlassClassificationEvaluator怎么用？Python evaluation.MulticlassClassificationEvaluator使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.ml.evaluation的用法示例。
在下文中一共展示了evaluation.MulticlassClassificationEvaluator方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from pyspark.ml import evaluation [as 别名]
# 或者: from pyspark.ml.evaluation import MulticlassClassificationEvaluator [as 别名]
def main(sc, spark):
# Load and vectorize the corpus
corpus = load_corpus(sc, spark)
vector = make_vectorizer().fit(corpus)
# Index the labels of the classification
labelIndex = StringIndexer(inputCol="label", outputCol="indexedLabel")
labelIndex = labelIndex.fit(corpus)
# Split the data into training and test sets
training, test = corpus.randomSplit([0.8, 0.2])
# Create the classifier
clf = LogisticRegression(
maxIter=10, regParam=0.3, elasticNetParam=0.8,
family="multinomial", labelCol="indexedLabel", featuresCol="tfidf")
# Create the model
model = Pipeline(stages=[
vector, labelIndex, clf
]).fit(training)
# Make predictions
predictions = model.transform(test)
predictions.select("prediction", "indexedLabel", "tfidf").show(5)
# Select (prediction, true label) and compute test error
evaluator = MulticlassClassificationEvaluator(
labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Test Error = %g" % (1.0 - accuracy))
gbtModel = model.stages[2]
print(gbtModel) # summary only