Regression models in Spark in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Linear Regression Model Training in Spark

What will be the output type of the variable model after running the following Spark ML code?

Apache Spark

from pyspark.ml.regression import LinearRegression

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

A A LinearRegressionModel object representing the trained model

B A DataFrame containing predictions on the training data

C A tuple containing coefficients and intercept values

D A SparkSession object

💡 Hint

The fit() method returns the trained model object, not predictions or raw data.

The fit() method of LinearRegression returns a LinearRegressionModel object that contains the learned coefficients and intercept. Predictions require calling transform() on this model.

❓ Model Choice

intermediate

Choosing the Correct Regression Model for Spark

You want to predict a continuous target variable using Spark ML. Which model should you choose?

A LogisticRegression

B DecisionTreeClassifier

C KMeans

D LinearRegression

💡 Hint

Regression models predict continuous values, classification models predict categories.

LinearRegression is used for predicting continuous numeric values. LogisticRegression and DecisionTreeClassifier are for classification. KMeans is for clustering.

❓ Metrics

advanced

Evaluating Regression Model Performance in Spark

After training a regression model in Spark, which metric is best to evaluate how close the predicted values are to the actual values?

A Root Mean Squared Error (RMSE)

B Area Under ROC Curve

C Accuracy

D F1 Score

💡 Hint

Regression metrics measure error between predicted and actual numeric values.

RMSE measures the average magnitude of prediction errors in regression. Accuracy, AUC, and F1 Score are classification metrics.

🔧 Debug

advanced

Identifying the Error in Spark Regression Code

What error will the following Spark code raise?

Apache Spark

from pyspark.ml.regression import LinearRegression

lr = LinearRegression(featuresCol='features', labelCol='label')
model = lr.fit(data)
predictions = model.transform('data')

A No error, code runs successfully

B NameError because 'model' is not defined

C TypeError because 'data' is passed as a string instead of a DataFrame

D ValueError because featuresCol is missing

💡 Hint

Check the argument type passed to transform().

The transform() method expects a DataFrame, but a string 'data' is passed, causing a TypeError.

🧠 Conceptual

expert

Effect of Regularization Parameter in Spark Linear Regression

In Spark's LinearRegression, what is the effect of increasing the regularization parameter regParam?

A It controls the number of iterations for convergence

B It decreases model complexity and helps prevent overfitting

C It changes the learning rate during training

D It increases model complexity and may cause overfitting

💡 Hint

Regularization adds a penalty to large coefficients to simplify the model.

Increasing regParam adds stronger penalty on coefficients, reducing their size and complexity, which helps prevent overfitting.

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