# COMMAND ----------
petalPredictions = petalModels[0].transform(irisPetal)
display(petalPredictions)
# COMMAND ----------
# MAGIC %md
# MAGIC Next, we'll evaluate the model using the `RegressionEvaluator`.
# COMMAND ----------
from pyspark.ml.evaluation import RegressionEvaluator
regEval = RegressionEvaluator().setLabelCol('petalWidth')
print regEval.explainParams()
# COMMAND ----------
# MAGIC %md
# MAGIC The default value for `RegressionEvaluator` is root mean square error (RMSE). Let's view that first.
# COMMAND ----------
print regEval.evaluate(petalPredictions)
# COMMAND ----------
# MAGIC %md
# MAGIC `RegressionEvaluator` also supports mean square error (MSE), \\( r^2 \\), and mean absolute error (MAE). We'll view the \\( r^2 \\) metric next.
# label:
predictions.printSchema()
predictions.show(5)
# ## Evaluate the linear regression model on the test data
# Import the `RegressionEvaluator` class from the `pyspark.ml.evaluation` module:
from pyspark.ml.evaluation import RegressionEvaluator
# Create an instance of the `RegressionEvaluator` class:
evaluator = RegressionEvaluator(predictionCol="prediction",
labelCol="duration",
metricName="r2")
# Call the `explainParams` method to see other metrics:
print(evaluator.explainParams())
# Use the `evaluate` method to compute the metric on the `predictions` DataFrame:
evaluator.evaluate(predictions)
# Use the `setMetricName` method to change the metric:
evaluator.setMetricName("rmse").evaluate(predictions)
# **Note:** You can also use the `evaluate` method of the `LinearRegressionModel` class.
# ## Plot the linear regression model
def plot_lr_model():
pdf = predictions.sample(withReplacement=False, fraction=0.1,
seed=34512).toPandas()
# COMMAND ----------
petalPredictions = petalModels[0].transform(irisPetal)
display(petalPredictions)
# COMMAND ----------
# MAGIC %md
# MAGIC Next, we'll evaluate the model using the `RegressionEvaluator`.
# COMMAND ----------
from pyspark.ml.evaluation import RegressionEvaluator
regEval = RegressionEvaluator().setLabelCol('petalWidth')
print regEval.explainParams()
# COMMAND ----------
# MAGIC %md
# MAGIC The default value for `RegressionEvaluator` is root mean square error (RMSE). Let's view that first.
# COMMAND ----------
print regEval.evaluate(petalPredictions)
# COMMAND ----------
# MAGIC %md
# MAGIC `RegressionEvaluator` also supports mean square error (MSE), \\( r^2 \\), and mean absolute error (MAE). We'll view the \\( r^2 \\) metric next.
