Impara Evaluating Regression Models

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Classification metrics like accuracy and AUC do not apply to regression. Instead, you measure how far predictions are from the true values using error-based metrics.

Key Metrics

RMSE (Root Mean Squared Error) – square root of the average squared error. Penalizes large errors more heavily. In the same units as the label (minutes);
MAE (Mean Absolute Error) – average absolute difference between predicted and actual values. More robust to outliers than RMSE;
R² (R-squared) – proportion of variance in the label explained by the model. Ranges from 0 to 1 – higher is better. A value of 1 means perfect prediction.

Evaluating with MLlib


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import urllib.request
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, floor
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, VectorAssembler, StandardScaler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.evaluation import RegressionEvaluator

urllib.request.urlretrieve(
    "https://staging-content-media-cdn.codefinity.com/courses/aa80ac56-0d50-49e8-9231-2c2374cd3e9d/flights.csv",
    "flights.csv"
)

spark = SparkSession.builder \
    .appName("RegressionEval") \
    .master("local[*]") \
    .getOrCreate()

flights_df = spark.read.csv("flights.csv", header=True, inferSchema=True) \
    .fillna(0, subset=["DEPARTURE_DELAY", "ARRIVAL_DELAY", "DISTANCE", "SCHEDULED_TIME"])

flights_df = flights_df \
    .withColumn("LABEL", col("ARRIVAL_DELAY").cast("double")) \
    .withColumn("DEPARTURE_HOUR", floor(col("SCHEDULED_DEPARTURE") / 100).cast("integer")) \
    .withColumn("IS_WEEKEND", (col("DAY_OF_WEEK") >= 6).cast("integer"))

train_df, test_df = flights_df.randomSplit([0.8, 0.2], seed=42)

indexer = StringIndexer(inputCol="AIRLINE", outputCol="AIRLINE_IDX")
assembler = VectorAssembler(
    inputCols=["DEPARTURE_DELAY", "DISTANCE", "SCHEDULED_TIME", "DEPARTURE_HOUR", "IS_WEEKEND", "AIRLINE_IDX"],
    outputCol="FEATURES_RAW"
)
scaler = StandardScaler(inputCol="FEATURES_RAW", outputCol="FEATURES", withMean=True, withStd=True)
lr = LinearRegression(featuresCol="FEATURES", labelCol="LABEL", maxIter=10, regParam=0.1)

model = Pipeline(stages=[indexer, assembler, scaler, lr]).fit(train_df)
predictions = model.transform(test_df)

# Evaluating with multiple metrics
evaluator = RegressionEvaluator(labelCol="LABEL", predictionCol="prediction")

for metric in ["rmse", "mae", "r2"]:
    evaluator.setMetricName(metric)
    print(f"{metric.upper()}: {evaluator.evaluate(predictions):.4f}")

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