Databricks Databricks-Machine-Learning-Associate Dumps

To use the pandas API on Spark, which is designed to bridge the gap between the simplicity of pandas and the scalability of Spark, the correct approach involves importing the pyspark.pandas (recently renamed topandas_api_on_spark) module and converting a Spark DataFrame to a pandas-on-Spark DataFrame using this API. The provided syntax correctly initializes a pandas-on-Spark DataFrame, allowing the data scientist to work with the familiar pandas-like API on large datasets managed by Spark.

References

• Pandas API on Spark Documentation:https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark. It allows users to perform pandas operations on large datasets using Spark's distributed compute capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and scalability.

References

• pandas API on Spark documentation:https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

TheOneHotEncoderin Spark ML requires numerical indices as inputs rather than string labels. Therefore, you need to first convert the string columns to numerical indices usingStringIndexer. After that, you can applyOneHotEncoderto these indices.

Corrected code:

frompyspark.ml.featureimportStringIndexer, OneHotEncoder# Convert string column to indexindexers = [StringIndexer(inputCol=col, outputCol=col+"_index")forcolininput_columns] indexer_model = Pipeline(stages=indexers).fit(features_df) indexed_features_df = indexer_model.transform(features_df)# One-hot encode the indexed columnsohe = OneHotEncoder(inputCols=[col+"_index"forcolininput_columns], outputCols=output_columns) ohe_model = ohe.fit(indexed_features_df) ohe_features_df = ohe_model.transform(indexed_features_df)

References:

• PySpark ML Documentation

When evaluating the RMSE for a model that predicts log-transformed prices, the predictions need to be transformed back to the original scale to obtain an RMSE that is comparable with the actual price values. This is done by exponentiating the predictions before computing the RMSE. The RMSE should be computed on the same scale as the original data to provide a meaningful measure of error.

References:

• Databricks documentation on regression evaluation: Regression Evaluation

A train-validation split is often preferred over k-fold cross-validation (with k > 2) when computational efficiency is a concern. With a train-validation split, only two models (one on the training set and one on the validation set) are trained, whereas k-fold cross-validation requires training k models (one for each fold).

This reduction in the number of models trained can save significant computational resources and time, especially when dealing with large datasets or complex models.

References:

• Model Evaluation with Train-Test Split

The F1 score is the harmonic mean of precision and recall and is particularly useful in situations where there is a significant imbalance between positive and negative classes. When there is a class imbalance, accuracy can be misleading because a model can achieve high accuracy by simply predicting the majority class. The F1 score, however, provides a better measure of the test's accuracy in terms of both false positives and false negatives.

Specifically, the F1 score should be used over accuracy when:

• There is a significant imbalance between positive and negative classes.
• Avoiding false negatives is a priority, meaning recall (the ability to detect all positive instances) is crucial.

In this scenario, the F1 score balances both precision (the ability to avoid false positives) and recall, providing a more meaningful measure of a model’s performance under these conditions.

References:

• Databricks documentation on classification metrics: Classification Metrics

When using theHyperoptlibrary withfmin, the goal is to find the minimum of the objective function. Since you are usingcross_val_scoreto calculate the R2 score which is a measure of the proportion of the variance for a dependent variable that's explained by an independent variable(s) in a regression model, higher values are better. However,fminseeks to minimize the objective function, so to align withfmin's goal, you should return the negative of the R2 score (-r2). This way, by minimizing the negative R2,fminis effectively maximizing the R2 score, which can lead to a more accurate model.

References

• Hyperopt Documentation: http://hyperopt.github.io/hyperopt/
• Scikit-Learn documentation on model evaluation: https://scikit-learn.org/stable/modules/model_evaluation.html

The pandas API on Spark provides a way to scale pandas operations to big data while minimizing the need for refactoring existing pandas code. It allows users to run pandas operations on Spark DataFrames, leveraging Spark’s distributed computing capabilities to handle large datasets more efficiently. This approach requires minimal changes to the existing code, making it a convenient option for scaling pandas-based feature engineering notebooks.

References:

• Databricks documentation on pandas API on Spark: pandas API on Spark

To speed up the model tuning process when dealing with a large number of model configurations, parallelizing the hyperparameter search using Hyperopt is an effective approach. Hyperopt provides tools likeSparkTrialswhich can run hyperparameter optimization in parallel across a Spark cluster.

Example:

fromhyperoptimportfmin, tpe, hp, SparkTrials search_space = {'x': hp.uniform('x',0,1),'y': hp.uniform('y',0,1) }defobjective(params):returnparams['x'] **2+ params['y'] **2spark_trials = SparkTrials(parallelism=4) best = fmin(fn=objective, space=search_space, algo=tpe.suggest, max_evals=100, trials=spark_trials)

References:

• Hyperopt Documentation

TheSparkTrialsclass in the Hyperopt library allows for parallel hyperparameter optimization on a Spark cluster. This enables efficient tuning of hyperparameters by distributing the optimization process across multiple nodes in a cluster.

fromhyperoptimportfmin, tpe, hp, SparkTrials search_space = {'x': hp.uniform('x',0,1),'y': hp.uniform('y',0,1) }defobjective(params):returnparams['x'] **2+ params['y'] **2spark_trials = SparkTrials(parallelism=4) best = fmin(fn=objective, space=search_space, algo=tpe.suggest, max_evals=100, trials=spark_trials)

References:

• Hyperopt Documentation

One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.References:

• Spark MLlib Documentation (Decision Trees and Quantile Binning).

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.

References:

• Databricks documentation on Random Forest: Random Forest in Spark ML

The correct method to randomly split a Spark DataFrame into training and test sets is by using therandomSplitmethod. This method allows you to specify the proportions for the split as a list of weights and returns multiple DataFrames according to those weights. This is directly intended for splitting DataFrames randomly and is the appropriate choice for preparing data for training and testing in machine learning workflows.References:

• Apache Spark DataFrame API documentation (DataFrame Operations: randomSplit).

In the provided code block, theImputerobject is created but not fitted on the data to generate anImputerModel. Thetransformmethod is being called directly on theImputerobject, which does not yet contain the fitted median values needed for imputation. The correct approach is to fit the imputer on the dataset first.

Corrected code:

imputer = Imputer( strategy="median", inputCols=input_columns, outputCols=output_columns ) imputer_model = imputer.fit(features_df)# Fit the imputer to the dataimputed_features_df = imputer_model.transform(features_df)# Transform the data using the fitted imputer

References:

• PySpark ML Documentation

To use the pandas API on Spark, the data scientist can run the following code block:

importpyspark.pandasasps df = ps.DataFrame(spark_df)

This code imports the pandas API on Spark and converts the Spark DataFramespark_dfinto a pandas-on-Spark DataFrame, allowing the data scientist to use familiar pandas functions for further feature engineering.

References:

• Databricks documentation on pandas API on Spark: pandas API on Spark

To compute the root mean-squared-error (RMSE) of a linear regression model using Spark ML, theRegressionEvaluatorclass is used. TheRegressionEvaluatoris specifically designed for regression tasks and can calculate various metrics, including RMSE, based on the columns containing predictions and actual values.

The correct code block to compute RMSE from thepreds_dfDataFrame is:

regression_evaluator = RegressionEvaluator( predictionCol="prediction", labelCol="actual", metricName="rmse") rmse = regression_evaluator.evaluate(preds_df)

This code creates an instance ofRegressionEvaluator, specifying the prediction and label columns, as well as the metric to be computed ("rmse"). It then evaluates the predictions inpreds_dfand assigns the resulting RMSE value to thermsevariable.

Options A and B incorrectly useBinaryClassificationEvaluator, which is not suitable for regression tasks. Option D also incorrectly usesBinaryClassificationEvaluator.

References:

• PySpark ML Documentation

To improve the likelihood of a more accurate model, the data scientist can increasenum_evalsto 100. Increasing the number of evaluations allows the hyperparameter tuning process to explore a larger search space and evaluate more combinations of hyperparameters, which increases the chance of finding a more optimal set of hyperparameters for the model.

References:

• Databricks documentation on hyperparameter tuning: Hyperparameter Tuning

Including the entire pipeline as the estimator in the cross-validation process means that all stages of the pipeline, including data preprocessing steps like string indexing and vector assembling, will be refit or retransformed for each fold of the cross-validation. This results in a longer runtime because each fold requires re-execution of these preprocessing steps, which can be computationally expensive.

If only the random forest regressor (rfr) were included as the estimator, the preprocessing steps would be performed once, and only the model fitting would be repeated for each fold, significantly reducing the computational overhead.

References:

• Databricks documentation on cross-validation: Cross Validation

To filter rows in a Spark DataFrame based on a condition, thefiltermethod is used. In this case, the condition is that the value in the "discount" column should be less than or equal to 0. The correct syntax uses thefiltermethod along with thecolfunction frompyspark.sql.functions.

Correct code:

frompyspark.sql.functionsimportcol filtered_df = spark_df.filter(col("discount") <=0)

Option A and D use Pandas syntax, which is not applicable in PySpark. Option B is closer but misses the use of thecolfunction.

References:

• PySpark SQL Documentation

The user wants to specify a search space for hyperparameters and let the tuning process randomly select values.GridSearchCVsystematically tries every combination of the provided hyperparameter values, which can be computationally expensive and time-consuming.RandomizedSearchCV, on the other hand, samples hyperparameters from a distribution for a fixed number of iterations. This approach is usually faster and still can find very good parameters, especially when the search space is large or includes distributions.

References

• Scikit-Learn documentation on hyperparameter tuning: https://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization

Using an iterator in thepandas_udfensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.

References:

• Databricks documentation on pandas UDFs: Pandas UDFs

The code block provided by the machine learning engineer will perform the desired inference when the Feature Store feature set was logged with the model at model_uri. This ensures that all necessary feature transformations and metadata are available for the model to make predictions. The Feature Store in Databricks allows for seamless integration of features and models, ensuring that the required features are correctly used during inference.

References:

• Databricks documentation on Feature Store: Feature Store in Databricks