Hands-On Classification with Spark MLlib: From Data to Predictions#

What is Classification?#

Classification is a supervised learning problem where the goal is to predict a discrete class label. For example, you might have:

• Binary Classification: Is this email spam or not spam? (Labels: 1 or 0)
• Multi-Class Classification: Which digit is depicted in an image (0 through 9)?

Typical Workflow#

1. Data Collection: Pull data from your data sources (files, databases, streams).
2. Data Preparation: Clean and preprocess data, handle missing values, select meaningful features.
3. Feature Engineering: Transform raw data into numerical feature vectors.
4. Model Training: Use training data to learn classification boundaries or rules.
5. Model Evaluation: Use metrics such as accuracy, F1-score, precision, and recall to measure performance.
6. Tuning and Deployment: Refine hyperparameters, then deploy your model to production systems.

In Spark MLlib, these steps align well with the DataFrame-based pipeline concept. You’ll transform your input DataFrame with a sequence of operations, culminating in a model ready for predictions.

Loading Data#

Spark can read data from a variety of sources:

• Local files
• Distributed file systems (e.g., HDFS)
• Cloud storage (S3, Azure Blob)
• JDBC connections to relational databases

For structured data (like CSV, TSV, or JSON), you can use:

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df = spark.read \
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    .option("inferSchema", "true") \
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    .option("header", "true") \
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    .csv("path/to/your_data.csv")
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df.printSchema()
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df.show(5)

Suppose we have a dataset of customer transactions, containing columns like:

• age (numeric)
• income (numeric)
• gender (categorical)
• country (string)
• purchased (binary label, 0 or 1)

You might see a schema like:

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root
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 |-- age: integer (nullable = true)
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 |-- income: double (nullable = true)
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 |-- gender: string (nullable = true)
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 |-- country: string (nullable = true)
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 |-- purchased: integer (nullable = true)

Handling Missing Values#

Large datasets often contain missing or invalid entries. In Spark:

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from pyspark.sql.functions import col
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# Drop rows missing any value
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df_clean = df.na.drop()
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# Or fill with a specific value
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df_filled = df.na.fill({"income": 0})

Alternatively, you can use advanced imputation techniques (e.g., a mean or median). Spark also offers Imputer for numerical columns:

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from pyspark.ml.feature import Imputer
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imputer = Imputer(
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    inputCols=["income"],
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    outputCols=["income_imputed"]
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).setStrategy("median")
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df_imputed = imputer.fit(df).transform(df)

Basic Exploratory Analysis#

While Spark is not primarily an exploratory tool, you can still do some quick queries and computations:

1. View summary statistics:

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   df.describe(['age', 'income']).show()
   

2. Group by categories:

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   df.groupBy("gender").count().show()
   

For more in-depth analysis or data visualization, you might sample a portion of your data and load it into a Pandas DataFrame or a plotting library. But for big data classification tasks, Spark’s distributed engine will handle the grunt work.

Why Feature Engineering?#

Machine learning models consume numbers (vectors) as input. However, real-world data has categorical columns, text, images, and other non-numerical formats. Feature engineering transforms raw data into numerical features that the model can understand.

Categorical Encoding#

In Spark MLlib, you typically convert categorical columns into numeric. Two common approaches:

1. StringIndexer: Converts categorical strings into numeric indices.

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   from pyspark.ml.feature import StringIndexer
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   indexer = StringIndexer(inputCol="gender", outputCol="gender_index")
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   df_indexed = indexer.fit(df).transform(df)
   

   This yields a column named gender_index mapping each category (e.g., male, female) to a unique numeric index.

2. OneHotEncoder: Converts the numeric index into a sparse vector (one-hot encoding).

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   from pyspark.ml.feature import OneHotEncoder
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   encoder = OneHotEncoder(inputCols=["gender_index"],
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                           outputCols=["gender_encoded"])
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   df_encoded = encoder.fit(df_indexed).transform(df_indexed)
   

   This yields a “vector” representation, e.g., [1.0, 0.0] for male, [0.0, 1.0] for female.

Assembling Features#

Eventually, you need a single column (traditionally named "features") containing the vector of all your input variables. You can use VectorAssembler:

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from pyspark.ml.feature import VectorAssembler
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assembler = VectorAssembler(
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    inputCols=["age", "income", "gender_encoded"],
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    outputCol="features"
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)
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final_df = assembler.transform(df_encoded)

Your final dataset might look like this:

Logistic Regression#

Logistic Regression is a fundamental classifier. Despite its name, it’s used for classification, not regression. It models the probability that a data point belongs to a particular class.

Here’s how to train a Logistic Regression classifier in Spark:

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from pyspark.ml.classification import LogisticRegression
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# Assume final_df has columns [features, purchased]
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# We'll rename "purchased" to "label" for convenience
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train_df = final_df.withColumnRenamed("purchased", "label")
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# Split data into training and test
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train_data, test_data = train_df.randomSplit([0.8, 0.2], seed=42)
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lr = LogisticRegression(featuresCol="features", labelCol="label")
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lr_model = lr.fit(train_data)
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# Evaluate on the test data
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predictions_lr = lr_model.transform(test_data)
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predictions_lr.select("features", "label", "prediction", "probability").show(5)
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# Evaluate performance
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from pyspark.ml.evaluation import MulticlassClassificationEvaluator
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evaluator = MulticlassClassificationEvaluator(labelCol="label",
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                                              predictionCol="prediction",
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                                              metricName="accuracy")
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accuracy = evaluator.evaluate(predictions_lr)
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print(f"Logistic Regression Accuracy: {accuracy:.2f}")

The code snippet does the following:

1. Renames the “purchased” column to “label,” which Spark expects for supervised learning.
2. Splits the data into training and test sets.
3. Trains the LogisticRegression model.
4. Makes predictions on the test set.
5. Evaluates the accuracy of the classifier.

You can also examine coefficients and intercept for logistic regression. Each feature’s coefficient indicates how much it influences the log-odds of the outcome.

Decision Tree#

Decision Trees divide your feature space into rectangular regions using hierarchical, if-then rules. Although they can overfit easily, they’re still quite intuitive.

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from pyspark.ml.classification import DecisionTreeClassifier
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dt = DecisionTreeClassifier(featuresCol="features", labelCol="label")
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dt_model = dt.fit(train_data)
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predictions_dt = dt_model.transform(test_data)
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accuracy_dt = evaluator.evaluate(predictions_dt)
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print(f"Decision Tree Accuracy: {accuracy_dt:.2f}")
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# Display the tree (a simple text representation)
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print(dt_model.toDebugString)

Decision trees are straightforward to interpret by looking at the tree structure, which can be especially useful if you need model transparency for compliance or debugging.

Random Forest#

A Random Forest is an ensemble of decision trees. Each tree is trained on a bootstrap sample of the data, and random subsets of features are considered at each split. This approach reduces overfitting and often significantly improves accuracy compared to a single decision tree.

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from pyspark.ml.classification import RandomForestClassifier
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rf = RandomForestClassifier(featuresCol="features", labelCol="label", numTrees=100)
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rf_model = rf.fit(train_data)
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predictions_rf = rf_model.transform(test_data)
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accuracy_rf = evaluator.evaluate(predictions_rf)
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print(f"Random Forest Accuracy: {accuracy_rf:.2f}")

When dealing with massive data, consider adjusting the maxDepth, numTrees, and subsamplingRate to speed up training and avoid memory issues.

Gradient-Boosted Trees#

Gradient-Boosted Trees (GBTs) are another ensemble approach. Rather than training all trees independently (like in random forests), GBT builds each new tree to correct errors of the previous ensemble. This often yields highly accurate models at the cost of additional tuning.

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from pyspark.ml.classification import GBTClassifier
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gbt = GBTClassifier(featuresCol="features", labelCol="label", maxIter=50)
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gbt_model = gbt.fit(train_data)
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predictions_gbt = gbt_model.transform(test_data)
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accuracy_gbt = evaluator.evaluate(predictions_gbt)
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print(f"GBT Accuracy: {accuracy_gbt:.2f}")

Because each subsequent tree “boosts” the performance of the entire ensemble, hyperparameters like maxIter (the number of iterations) and maxDepth significantly impact results.

Naive Bayes#

For text classification or scenarios where features are assumed (or approximated) to be conditionally independent, Naive Bayes can be extremely fast and surprisingly effective.

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from pyspark.ml.classification import NaiveBayes
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nb = NaiveBayes(featuresCol="features", labelCol="label")
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nb_model = nb.fit(train_data)
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predictions_nb = nb_model.transform(test_data)
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accuracy_nb = evaluator.evaluate(predictions_nb)
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print(f"Naive Bayes Accuracy: {accuracy_nb:.2f}")

Hyperparameter Tuning#

Each ML algorithm includes parameters that can significantly affect performance. Examples:

• Logistic Regression: regParam, elasticNetParam
• Decision Tree: maxDepth, minInstancesPerNode
• Random Forest: numTrees, maxDepth, subsamplingRate
• GBT: maxIter, maxDepth

You can systematically search for the best combination of parameters via:

1. Grid Search: Exhaustively try every combination from a predefined range.
2. Random Search: Randomly sample parameter combinations.

In Spark, this is facilitated by CrossValidator or TrainValidationSplit. Below is an example of using CrossValidator with a simple parameter grid for Logistic Regression:

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from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
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lr = LogisticRegression(featuresCol="features", labelCol="label")
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paramGrid = ParamGridBuilder() \
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    .addGrid(lr.regParam, [0.01, 0.1, 1.0]) \
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    .addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0]) \
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    .build()
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cv = CrossValidator(estimator=lr,
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                    estimatorParamMaps=paramGrid,
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                    evaluator=evaluator,
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                    numFolds=5)  # 5-fold cross-validation
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cv_model = cv.fit(train_data)
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best_model = cv_model.bestModel
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predictions_cv = best_model.transform(test_data)
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accuracy_cv = evaluator.evaluate(predictions_cv)
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print(f"Best CV Accuracy: {accuracy_cv:.2f}")

This code:

1. Creates a parameter grid (regParam and elasticNetParam).
2. Uses CrossValidator to train multiple models with different combinations.
3. Selects the best model based on the chosen evaluation metric (accuracy here).
4. Evaluates the best model on the test data.

Pipelines#

Spark’s Pipeline API lets you combine multiple stages (indexing, encoding, assembling, training, etc.) into a single object. This is especially handy for hyperparameter tuning where transformations must be applied exactly the same way during each fold of cross-validation.

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from pyspark.ml import Pipeline
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# Suppose we have two transformations: indexer, assembler, and one classifier
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indexer = StringIndexer(inputCol="gender", outputCol="gender_index")
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encoder = OneHotEncoder(inputCols=["gender_index"], outputCols=["gender_encoded"])
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assembler = VectorAssembler(inputCols=["age", "income", "gender_encoded"], outputCol="features")
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lr = LogisticRegression(featuresCol="features", labelCol="label")
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pipeline = Pipeline(stages=[indexer, encoder, assembler, lr])
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# Now create a parameter grid
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paramGrid = ParamGridBuilder() \
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    .addGrid(lr.regParam, [0.01, 0.1]) \
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    .addGrid(lr.elasticNetParam, [0.0, 1.0]) \
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    .build()
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cv = CrossValidator(estimator=pipeline,
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                    estimatorParamMaps=paramGrid,
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                    evaluator=evaluator,
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                    numFolds=3)
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# Assume df has the columns [age, income, gender, purchased] (purchased -> label)
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df_prepared = df.withColumnRenamed("purchased", "label")
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train_data, test_data = df_prepared.randomSplit([0.8, 0.2], seed=42)
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cv_model = cv.fit(train_data)
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predictions_pipeline = cv_model.transform(test_data)
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accuracy_pipeline = evaluator.evaluate(predictions_pipeline)
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print(f"Pipeline CV Accuracy: {accuracy_pipeline:.2f}")

The pipeline ensures that our transformations are applied consistently. If you have more complex feature engineering steps or multiple encoders, this approach keeps your code organized and reproducible.

Feature Selection#

While feature engineering generates numeric vectors, you can end up with a large number of features. Some of them may not be relevant (or can even be detrimental). Spark MLlib provides feature selection methods such as:

• ChiSqSelector: Selects features based on the Chi-Squared test with respect to the label.
• PCA (Principal Component Analysis): A dimensionality reduction technique (though more commonly used in unsupervised contexts).

Example with ChiSqSelector:

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from pyspark.ml.feature import ChiSqSelector
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selector = ChiSqSelector(numTopFeatures=3,
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                         featuresCol="features",
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                         outputCol="selectedFeatures",
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                         labelCol="label")
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df_selected = selector.fit(final_df).transform(final_df)

Handling Imbalanced Data#

Real-world classification problems can be plagued by imbalanced classes (e.g., fraud detection, where most transactions are legitimate). Potential strategies include:

• Under-sampling or over-sampling: Adjust the dataset to make class distribution more balanced.
• Synthetic data generation: Methods like SMOTE can create synthetic minority samples.
• Adjusting class weights: Some algorithms (like logistic regression) allow specifying class weights to give more emphasis to minority classes.

In Spark, you can set classWeightCol, or you might manually modify your dataset. For example:

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major_df = df.filter(col("label") == 0)
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minor_df = df.filter(col("label") == 1)
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ratio = major_df.count() / minor_df.count()
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minor_upsampled = minor_df.sample(withReplacement=True,
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                                  fraction=ratio,
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                                  seed=42)
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df_balanced = major_df.union(minor_upsampled)

Model Explainability#

Although tree-based models can be partially interpreted by tree structures or feature importances, extracting more in-depth insights (like Shapley values) might require integrating Spark with specialized libraries. Model explainability tools (e.g., ELI5, SHAP) can help you understand why the model makes particular predictions.

Streaming Data#

Spark Streaming (or Structured Streaming) allows you to perform classification in real-time. You can load new data from a streaming source, transform it with your pipeline, and use a previously trained model for predictions on live data. This is a more advanced and production-oriented scenario but extremely useful in time-sensitive tasks (e.g., anomaly detection in logs).