Hyperparameter tuning with CrossValidator in Apache Spark - Mini Project: Build & Apply

Hyperparameter tuning with CrossValidator

📖 Scenario: You are working as a data scientist on a project to predict if a person earns more than $50K a year based on census data. You want to build a machine learning model using Apache Spark's MLlib. To get the best model, you will tune the hyperparameters of a Decision Tree classifier using CrossValidator.

🎯 Goal: Build a Decision Tree classification model and use CrossValidator to find the best hyperparameters for maxDepth and maxBins. Finally, print the best model's parameters.

📋 What You'll Learn

Create a DataFrame with sample census data

Set up a Decision Tree classifier

Create a ParamGridBuilder with maxDepth and maxBins values

Use CrossValidator with a BinaryClassificationEvaluator

Print the best model's maxDepth and maxBins

💡 Why This Matters

🌍 Real World

Hyperparameter tuning helps improve machine learning model accuracy by finding the best settings automatically.

💼 Career

Data scientists and machine learning engineers use hyperparameter tuning with tools like Spark to build better predictive models at scale.

1

Create the initial DataFrame

Create a Spark DataFrame called data with these exact rows and columns: ("age", "education_num", "income") with values (25, 10, 0), (38, 13, 1), (28, 9, 0), (44, 10, 1), (18, 7, 0).

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("HyperparameterTuning").getOrCreate()
# Create the DataFrame called data with the specified rows and columns
# Your code here

Hint

Use spark.createDataFrame with a list of tuples and specify the column names as a list.

2

Set up the Decision Tree classifier and ParamGrid

Create a Decision Tree classifier called dt with label column income and features column features. Then create a ParamGridBuilder called paramGrid that sets maxDepth to [2, 3] and maxBins to [20, 30].

Apache Spark

from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.tuning import ParamGridBuilder
# Create DecisionTreeClassifier dt with labelCol='income' and featuresCol='features'
# Create paramGrid with maxDepth=[2,3] and maxBins=[20,30]
# Your code here

Hint

Use DecisionTreeClassifier(labelCol="income", featuresCol="features") and ParamGridBuilder().addGrid(...).build().

3

Create the CrossValidator and fit the model

Create a CrossValidator called cv with estimator=dt, estimatorParamMaps=paramGrid, and a BinaryClassificationEvaluator with label column income. Then fit cv on a DataFrame called assembled_data that has a features vector column created from age and education_num.

Apache Spark

from pyspark.ml.feature import VectorAssembler
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.tuning import CrossValidator
# Create VectorAssembler to combine 'age' and 'education_num' into 'features'
# Create CrossValidator cv with estimator=dt, estimatorParamMaps=paramGrid, evaluator=BinaryClassificationEvaluator(labelCol='income')
# Fit cv on assembled_data
# Your code here

Hint

Use VectorAssembler to create features, then CrossValidator with the given parameters, and call fit().

4

Print the best model's hyperparameters

Print the best model's maxDepth and maxBins values from cvModel.bestModel using print statements.

Apache Spark

# Print the best model's maxDepth and maxBins
# Your code here

Hint

Use cvModel.bestModel._java_obj.getMaxDepth() and cvModel.bestModel._java_obj.getMaxBins() to get the values.

Practice

1. What is the main purpose of using CrossValidator in Apache Spark's machine learning pipeline?

easy

A. To visualize the model's performance metrics

B. To train the model on the entire dataset without validation

C. To clean and preprocess the input data

D. To find the best hyperparameters by testing multiple data splits

Solution

1. Step 1: Understand CrossValidator's role

   CrossValidator splits data into training and validation sets multiple times to evaluate model performance.

2. Step 2: Identify the purpose of hyperparameter tuning

   It tests different hyperparameter combinations to find the best model settings.

3. Final Answer:

   To find the best hyperparameters by testing multiple data splits -> Option D

4. Quick Check:

   CrossValidator = Best hyperparameters [OK]

Hint: CrossValidator tests parameters on data splits to find best model [OK]

Common Mistakes:

• Thinking CrossValidator trains on full data without validation
• Confusing CrossValidator with data cleaning tools
• Assuming CrossValidator is for visualization only

2. Which of the following is the correct way to create a parameter grid using ParamGridBuilder in Spark ML?

easy

A. paramGrid = ParamGridBuilder().addGrid(model.maxDepth, [2, 5]).build()

B. paramGrid = ParamGridBuilder.addGrid(model.maxDepth, [2, 5]).build()

C. paramGrid = ParamGridBuilder().addGrid(model.maxDepth = [2, 5]).build()

D. paramGrid = ParamGridBuilder().addGrid([2, 5], model.maxDepth).build()

Solution

1. Step 1: Recall ParamGridBuilder syntax

   ParamGridBuilder is instantiated, then addGrid is called with parameter and list of values.

2. Step 2: Check each option's syntax

   paramGrid = ParamGridBuilder().addGrid(model.maxDepth, [2, 5]).build() correctly calls ParamGridBuilder() then addGrid(model.maxDepth, [2, 5]) and finally build().

3. Final Answer:

   paramGrid = ParamGridBuilder().addGrid(model.maxDepth, [2, 5]).build() -> Option A

4. Quick Check:

   ParamGridBuilder() + addGrid(param, values) + build() [OK]

Hint: Use ParamGridBuilder() then addGrid(param, values) then build() [OK]

Common Mistakes:

• Calling addGrid without parentheses on ParamGridBuilder
• Using assignment inside addGrid instead of parameters
• Swapping parameter and values order in addGrid

3. Given the following code snippet, what will be the output of cvModel.bestModel.getMaxDepth()?

from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import BinaryClassificationEvaluator

model = DecisionTreeClassifier()
paramGrid = ParamGridBuilder().addGrid(model.maxDepth, [2, 4]).build()
evaluator = BinaryClassificationEvaluator()

cv = CrossValidator(estimator=model, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=2)
cvModel = cv.fit(trainingData)

best_depth = cvModel.bestModel.getMaxDepth()

medium

A. The average of 2 and 4, which is 3

B. 2 or 4 depending on which depth gave better validation metric

C. An error because getMaxDepth() is not a valid method

D. Always 2 because it is the first value in the grid

Solution

1. Step 1: Understand CrossValidator fitting

   CrossValidator tries all parameter combinations and selects the best model based on evaluator metric.

2. Step 2: Interpret bestModel.getMaxDepth()

   The bestModel will have maxDepth set to the value that gave the best validation score, either 2 or 4.

3. Final Answer:

   2 or 4 depending on which depth gave better validation metric -> Option B

4. Quick Check:

   bestModel maxDepth = best param tested [OK]

Hint: bestModel has hyperparameter with best validation score [OK]

Common Mistakes:

• Assuming bestModel always uses first grid value
• Thinking getMaxDepth() method does not exist
• Calculating average of grid values instead of selecting best

4. Identify the error in this code snippet for hyperparameter tuning with CrossValidator:

from pyspark.ml.classification import LogisticRegression
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import BinaryClassificationEvaluator

lr = LogisticRegression()
paramGrid = ParamGridBuilder().addGrid(lr.regParam, [0.1, 0.01])

evaluator = BinaryClassificationEvaluator()
cv = CrossValidator(estimator=lr, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=3)
cvModel = cv.fit(trainingData)

medium

A. Missing .build() call after ParamGridBuilder()

B. BinaryClassificationEvaluator cannot be used with LogisticRegression

C. numFolds must be at least 5 for CrossValidator

D. regParam values must be strings, not floats

Solution

1. Step 1: Check ParamGridBuilder usage

   ParamGridBuilder requires calling .build() to create the parameter grid list.

2. Step 2: Identify missing method call

   The code misses .build() after addGrid, so paramGrid is not a valid parameter map list.

3. Final Answer:

   Missing .build() call after ParamGridBuilder() -> Option A

4. Quick Check:

   ParamGridBuilder().addGrid(...).build() required [OK]

Hint: Always end ParamGridBuilder with .build() to create grid [OK]

Common Mistakes:

• Forgetting to call .build() after addGrid
• Using wrong evaluator for classification
• Setting numFolds less than 2 (valid but uncommon)

5. You want to tune two hyperparameters of a RandomForestClassifier: numTrees with values [10, 20] and maxDepth with values [5, 10]. How many total models will CrossValidator train if numFolds=3?

hard

A. 30 models

B. 6 models

C. 12 models

D. 60 models

Solution

1. Step 1: Calculate total hyperparameter combinations

   numTrees has 2 values, maxDepth has 2 values, so total combinations = 2 * 2 = 4.

2. Step 2: Multiply by number of folds

   CrossValidator trains each combination on each fold, so total models = 4 * 3 = 12.

3. Step 3: Re-check calculation

   Wait, the question asks how many total models trained. Each fold trains one model per combination, so total = combinations * folds = 4 * 3 = 12.

4. Final Answer:

   12 models -> Option C

5. Quick Check:

   Combinations * folds = 12 [OK]

Hint: Multiply parameter combos by folds for total models trained [OK]

Common Mistakes:

• Multiplying number of values incorrectly
• Forgetting to multiply by number of folds
• Confusing total models with total parameter combos