Python NaiveBayes.explainParamsの例

コード例 #1

rfClassifier = RandomForestClassifier()
print rfClassifier.explainParams()
trainedModel = rfClassifier.fit(bInput)

# COMMAND ----------

from pyspark.ml.classification import GBTClassifier
gbtClassifier = GBTClassifier()
print gbtClassifier.explainParams()
trainedModel = gbtClassifier.fit(bInput)

# COMMAND ----------

from pyspark.ml.classification import NaiveBayes
nb = NaiveBayes()
print nb.explainParams()
trainedModel = nb.fit(bInput.where("label != 0"))

# COMMAND ----------

from pyspark.mllib.evaluation import BinaryClassificationMetrics
out = trainedModel.transform(bInput)\
  .select("prediction", "label")\
  .rdd.map(lambda x: (float(x[0]), float(x[1])))
metrics = BinaryClassificationMetrics(out)

# COMMAND ----------

print metrics.areaUnderPR
print metrics.areaUnderROC

コード例 #2

ファイル: STDG_Ch_26_Py.py プロジェクト: neeraj-somani/SparkTheDefinitiveGuideBook

rfClassifier = RandomForestClassifier()
print(rfClassifier.explainParams())
trainedModel = rfClassifier.fit(bInput)

# COMMAND ----------

from pyspark.ml.classification import GBTClassifier
gbtClassifier = GBTClassifier()
print(gbtClassifier.explainParams())
trainedModel = gbtClassifier.fit(bInput)

# COMMAND ----------

from pyspark.ml.classification import NaiveBayes
nb = NaiveBayes()
print(nb.explainParams())
trainedModel = nb.fit(bInput.where("label != 0"))

# COMMAND ----------

from pyspark.mllib.evaluation import BinaryClassificationMetrics
out = trainedModel.transform(bInput)\
  .select("prediction", "label")\
  .rdd.map(lambda x: (float(x[0]), float(x[1])))
metrics = BinaryClassificationMetrics(out)

# COMMAND ----------

print(metrics.areaUnderPR)
print(metrics.areaUnderROC)
print("Receiver Operating Characteristic")