Hyperparameter tuning with CrossValidator in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the CrossValidator class from Spark ML.

Apache Spark

from pyspark.ml.tuning import [1]

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

ACrossValidator

BParamGridBuilder

CPipeline

DTrainValidationSplit

Attempts:

3 left

💡 Hint

Common Mistakes

Importing ParamGridBuilder instead of CrossValidator

Importing Pipeline which is unrelated to cross-validation

Importing TrainValidationSplit which is a different tuning method

The CrossValidator class is used for hyperparameter tuning with cross-validation in Spark ML.

2fill in blank

medium

Complete the code to create a parameter grid for tuning the maxDepth parameter of a DecisionTreeClassifier.

Apache Spark

paramGrid = ParamGridBuilder().addGrid(dt.[1], [2, 4, 6]).build()

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

AmaxBins

BminInstancesPerNode

CmaxDepth

Dimpurity

Attempts:

3 left

💡 Hint

Common Mistakes

Using maxBins which controls number of bins for features

Using impurity which controls split criterion

Using minInstancesPerNode which controls minimum samples per node

The maxDepth parameter controls the maximum depth of the decision tree and is commonly tuned.

3fill in blank

hard

Fix the error in the CrossValidator initialization by filling the correct evaluator parameter.

Apache Spark

cv = CrossValidator(estimator=dt, estimatorParamMaps=paramGrid, evaluator=[1], numFolds=3)

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ARegressionEvaluator()

BBinaryClassificationEvaluator()

CClusteringEvaluator()

DMulticlassClassificationEvaluator()

Attempts:

3 left

💡 Hint

Common Mistakes

Using RegressionEvaluator for classification

Using BinaryClassificationEvaluator for multiclass problems

Using ClusteringEvaluator which is for clustering tasks

For classification tasks with multiple classes, MulticlassClassificationEvaluator is the correct evaluator.

4fill in blank

hard

Fill both blanks to fit the CrossValidator model and get the best model.

Apache Spark

cvModel = cv.[1](trainData)
bestModel = cvModel.[2]

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Afit

BbestModel

Ctransform

Devaluate

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform instead of fit to train

Trying to call evaluate method which does not exist

Accessing bestModel before fitting

First, fit the CrossValidator on training data. Then access the bestModel attribute to get the best tuned model.

5fill in blank

hard

Fill all three blanks to evaluate the best model on test data and print the accuracy.

Apache Spark

predictions = bestModel.[1](testData)
evaluator = MulticlassClassificationEvaluator(labelCol='label', predictionCol='prediction', metricName=[2])
accuracy = evaluator.[3](predictions)
print(f"Test Accuracy = {accuracy}")

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

Atransform

B"accuracy"

Cevaluate

D"areaUnderROC"

Attempts:

3 left

💡 Hint

Common Mistakes

Using areaUnderROC metric for multiclass accuracy

Calling transform on evaluator instead of model

Using wrong method name instead of evaluate

Use transform to get predictions, set metricName to accuracy, and call evaluate to compute accuracy.

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