Regression models in Spark in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Linear Regression model in Spark.

Apache Spark

from pyspark.ml.regression import [1]

lr = [1]()

Drag options to blanks, or click blank then click option'

ALogisticRegression

BRandomForestRegressor

CDecisionTreeRegressor

DLinearRegression

Attempts:

3 left

💡 Hint

Common Mistakes

Using classification models like LogisticRegression instead of regression models.

Confusing regression with decision tree or random forest classes.

The LinearRegression class is used to create a linear regression model in Spark MLlib.

2fill in blank

medium

Complete the code to fit the Linear Regression model on the training data.

Apache Spark

model = lr.[1](trainingData)

Drag options to blanks, or click blank then click option'

Atransform

Bpredict

Cfit

Devaluate

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform instead of fit to train the model.

Trying to use predict which is not a method in Spark MLlib models.

The fit method trains the model on the given training data.

3fill in blank

hard

Fix the error in the code to evaluate the regression model using RMSE metric.

Apache Spark

from pyspark.ml.evaluation import RegressionEvaluator

evaluator = RegressionEvaluator(labelCol="label", predictionCol="prediction", metricName="[1]")

Drag options to blanks, or click blank then click option'

Aaccuracy

Brmse

Cf1

Dauc

Attempts:

3 left

💡 Hint

Common Mistakes

Using classification metrics like accuracy or f1 for regression evaluation.

Misspelling the metric name.

The rmse metric stands for Root Mean Squared Error, which is used to evaluate regression models.

4fill in blank

hard

Fill both blanks to create a DataFrame with features and label columns for regression.

Apache Spark

from pyspark.ml.linalg import Vectors

trainingData = spark.createDataFrame([
    (1.0, Vectors.dense([1])),
    (0.0, Vectors.dense([2]))
], ["label", "features"])

Drag options to blanks, or click blank then click option'

A[0.0, 1.1, 0.1]

B[1.0, 2.0, 3.0]

C[1.0, 0.1, -1.0]

D[2.0, 1.3, 0.5]

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-numeric values inside the vectors.

Mixing up label and features positions.

The features vectors must be numeric lists. Here, the first example uses [0.0, 1.1, 0.1] and the second uses [2.0, 1.3, 0.5] as feature values.

5fill in blank

hard

Fill both blanks to extract the coefficients, intercept, and RMSE of the trained model.

Apache Spark

coefficients = model.coefficients
intercept = model.[1]
rmse = evaluator.evaluate(model.[2](testData))
print(f"Coefficients: {coefficients}, Intercept: {intercept}, RMSE: {rmse}")

Drag options to blanks, or click blank then click option'

Aintercept

Btransform

Cpredict

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict which is not a Spark MLlib method.

Trying to call fit on test data instead of transform.

The model's intercept is accessed by intercept. To get predictions on test data, use transform. The RMSE is computed by evaluating these predictions.

Practice

1. What is the main purpose of regression models in Apache Spark ML?

easy

A. To predict continuous numerical values from input data

B. To classify data into categories

C. To cluster data points into groups

D. To reduce the dimensionality of data

Solution

1. Step 1: Understand regression model goal

   Regression models are designed to predict continuous numerical outcomes based on input features.

2. Step 2: Compare with other model types

   Classification predicts categories, clustering groups data, and dimensionality reduction simplifies features, so these are not regression tasks.

3. Final Answer:

   To predict continuous numerical values from input data -> Option A

4. Quick Check:

   Regression = Predict numbers [OK]

Hint: Regression predicts numbers, classification predicts categories [OK]

Common Mistakes:

• Confusing regression with classification
• Thinking regression clusters data
• Mixing regression with dimensionality reduction

2. Which of the following is the correct way to create a Linear Regression model in Spark ML using Python?

easy

A. from pyspark.ml.classification import LinearRegression; lr = LinearRegression()

B. from pyspark.ml.regression import LinearRegression; lr = LinearRegression()

C. from pyspark.ml.regression import LogisticRegression; lr = LogisticRegression()

D. from pyspark.ml.clustering import LinearRegression; lr = LinearRegression()

Solution

1. Step 1: Identify correct import for Linear Regression

   LinearRegression is in pyspark.ml.regression module, so import must be from there.

2. Step 2: Check class name and module match

   from pyspark.ml.regression import LinearRegression; lr = LinearRegression() correctly imports LinearRegression from regression module and creates an instance.

3. Final Answer:

   from pyspark.ml.regression import LinearRegression; lr = LinearRegression() -> Option B

4. Quick Check:

   LinearRegression import from regression module [OK]

Hint: Regression models import from pyspark.ml.regression [OK]

Common Mistakes:

• Importing LinearRegression from classification or clustering modules
• Using LogisticRegression instead of LinearRegression
• Wrong module import causing runtime errors

3. Given the following Spark code, what will be the output of predictions.select('prediction').collect()?

from pyspark.ml.regression import LinearRegression
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()
data = spark.createDataFrame([
  (1.0, 2.0),
  (2.0, 4.1),
  (3.0, 6.0)
], ['feature', 'label'])
lr = LinearRegression(featuresCol='feature', labelCol='label')
model = lr.fit(data)
predictions = model.transform(data)

medium

A. [Row(prediction=2.0), Row(prediction=4.1), Row(prediction=6.0)]

B. [Row(prediction=2.05), Row(prediction=4.1), Row(prediction=6.15)]

C. Error due to incorrect features column type

D. [Row(prediction=1.0), Row(prediction=2.0), Row(prediction=3.0)]

Solution

1. Step 1: Check features column type

   LinearRegression expects featuresCol to be a vector column, but here 'feature' is a single float, causing an error.

2. Step 2: Understand Spark ML input requirements

   Features must be Vector type; passing a float causes runtime error during fit or transform.

3. Final Answer:

   Error due to incorrect features column type -> Option C

4. Quick Check:

   Features must be Vector, not float [OK]

Hint: Features must be Vector type, not single float [OK]

Common Mistakes:

• Passing float instead of Vector for features
• Expecting predictions without proper feature vector
• Ignoring Spark ML input data format requirements

4. You have this Spark ML code snippet that throws an error:

lr = LinearRegression(featuresCol='features', labelCol='label')
model = lr.fit(trainingData)

The error says: "java.lang.IllegalArgumentException: requirement failed: Column features must be of type vector." What is the likely cause and fix?

medium

A. The trainingData is empty; load data before fitting

B. The 'label' column is missing; add a label column to the data

C. LinearRegression does not support 'features' as input; rename the column

D. The 'features' column is not a vector; fix by using VectorAssembler to create it

Solution

1. Step 1: Analyze error message

   The error says 'features' column must be a vector, so current 'features' column is not Vector type.

2. Step 2: Fix by creating vector features

   Use VectorAssembler to combine feature columns into a single vector column named 'features'.

3. Final Answer:

   The 'features' column is not a vector; fix by using VectorAssembler to create it -> Option D

4. Quick Check:

   Features must be Vector; use VectorAssembler [OK]

Hint: Use VectorAssembler to create vector features column [OK]

Common Mistakes:

• Ignoring the need for vector type in features column
• Renaming columns without fixing data type
• Assuming label column causes this error

5. You want to predict house prices using Spark ML Linear Regression. Your dataset has columns: size (square feet), bedrooms, and price</>. Which is the correct way to prepare features and train the model?

hard

A. Use VectorAssembler to combine 'size' and 'bedrooms' into 'features', then fit LinearRegression with labelCol='price'

B. Directly use 'size' as featuresCol and 'price' as labelCol in LinearRegression

C. Use 'price' as featuresCol and 'size' as labelCol in LinearRegression

D. Train LinearRegression without feature transformation on 'bedrooms' only

Solution

1. Step 1: Combine input features

   Multiple input columns ('size', 'bedrooms') must be combined into a single vector column using VectorAssembler.

2. Step 2: Train LinearRegression model

   Use the combined 'features' vector column as featuresCol and 'price' as labelCol to fit the model.

3. Final Answer:

   Use VectorAssembler to combine 'size' and 'bedrooms' into 'features', then fit LinearRegression with labelCol='price' -> Option A

4. Quick Check:

   Combine features vector, label is target [OK]

Hint: Combine all input features into vector before training [OK]

Common Mistakes:

• Using single feature without vector assembly
• Swapping label and features columns
• Ignoring feature vector requirement