Databricks-Machine-Learning-Associate Sample Questions Answers

In Spark ML, a transformer is an algorithm that can transform one DataFrame into another DataFrame. It takes a DataFrame as input and produces a new DataFrame as output. This transformation can involve adding new columns, modifying existing ones, or applying feature transformations. Examples of transformers in Spark MLlib include feature transformers likeStringIndexer,VectorAssembler, andStandardScaler.

References:

• Databricks documentation on transformers: Transformers in Spark ML

Gradient boosting is fundamentally an iterative algorithm where each new tree is built based on the errors of the previous ones. This sequential dependency makes it difficult to parallelize the training of trees in gradient boosting, as each step relies on the results from the preceding step. Parallelization in this context would undermine the core methodology of the algorithm, which depends on sequentially improving the model'sperformance with each iteration.References:

• Machine Learning Algorithms (Challenges with Parallelizing Gradient Boosting).

Gradient boosting is an ensemble learning technique that builds models in a sequential manner. Each new model corrects the errors made by the previous ones. This sequential dependency means that each iteration requires the results of the previous iteration to make corrections. Here is a step-by-step explanation of why this makes parallelization challenging:

• Sequential Nature: Gradient boosting builds one tree at a time. Each tree is trained to correct the residual errors of the previous trees. This requires the model to complete one iteration before starting the next.
• Dependence on Previous Iterations: The gradient calculation at each step depends on the predictions made by the previous models. Therefore, the model must wait until the previous tree has been fully trained and evaluated before starting to train the next tree.
• Difficulty in Parallelization: Because of this dependency, it is challenging to parallelize the training process. Unlike algorithms that process data independently in each step (e.g., random forests), gradient boosting cannot easily distribute the work across multiple processors or cores for simultaneous execution.

This iterative and dependent nature of the gradient boosting process makes it difficult to parallelize effectively.

References

• Gradient Boosting Machine Learning Algorithm
• Understanding Gradient Boosting Machines

In Spark ML, the linear regression model expects the feature column to be a vector type. However, if thefeaturescolumn in the DataFrametrain_dfis not already in this format (such as being a column of type UDT or a non-vectorized type), the engineerneeds to convert it to a vector column using a transformer likeVectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.

References

• Spark MLlib documentation forLinearRegression:https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression

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 find the run_id of the run with the best root-mean-square error (RMSE) in an MLflow experiment, the correct line of code to use is:

mlflow.search_runs( experiment_id, order_by=["metrics.rmse"] )["run_id"][0]

This line of code searches the runs in the specified experiment, orders them by the RMSE metric in ascending order (the lower the RMSE, the better), and retrieves the run_id of the best-performing run. Option C correctly represents this logic.

References

• MLflow documentation on tracking experiments: https://www.mlflow.org/docs/latest/python_api/mlflow.html#mlflow.search_runs

For large datasets with many variables, Spark ML distributes the training of a linear regression model using iterative optimization methods. Specifically, Spark ML employs algorithms such as Gradient Descent or L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) to iteratively minimize the loss function. These iterative methods are suitable for distributed computing environments and can handle large-scale data efficiently by partitioning the data across nodes in a cluster and performing parallel updates.References:

• Spark MLlib Documentation (Linear Regression with Iterative Optimization).

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).

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

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

In the context of Spark MLlib, an estimator refers to an algorithm which can be "fit" on a DataFrame to produce a model (referred to as a Transformer), which can then be used to transform one DataFrame into another, typically adding predictions or model scores. This is a fundamental concept in machine learning pipelines in Spark, where the workflow includes fitting estimators to data to produce transformers.

References

• Spark MLlib Documentation:https://spark.apache.org/docs/latest/ml-pipeline.html#estimators

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

The pandas API on Spark DataFrames are made up of Spark DataFrames with additional metadata. The pandas API on Spark aims to provide the pandas-like experience with the scalability and distributed nature of Spark. It allows users to work with pandas functions on large datasets by leveraging Spark’s underlying capabilities.

References:

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

To view the notebook that was run to create an MLflow run, you can click the "Source" link in the row corresponding to the run in the MLflow experiment page. The "Source" link provides a direct reference to the source notebook or script that initiated the run, allowing you to review the code and methodology used in the experiment. This feature is particularly useful for reproducibility and for understanding the context of the experiment.References:

• MLflow Documentation (Viewing Run Sources and Notebooks).

If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.

In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.

References:

• Model Ensemble Techniques

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

By creating a binary feature variable for each feature with missing values to indicate whether a value has been imputed, the data scientist can preserve information about the original state of the data. This approach maintains the integrity of the dataset by marking which values are original and which are synthetic (imputed). Here are the steps to implement this approach:

• Identify Missing Values:Determine which features contain missing values.
• Impute Missing Values:Continue with median imputation or choose another method (mean, mode, regression, etc.) to fill missing values.
• Create Indicator Variables:For each feature that had missing values, add a new binary feature. This feature should be '1' if the original value was missing and imputed, and '0' otherwise.
• Data Integration:Integrate these new binary features into the existing dataset. This maintains a record of where data imputation occurred, allowing models to potentially weight these observations differently.
• Model Adjustment:Adjust machine learning models to account for these new features, which might involve considering interactions between these binary indicators and other features.

References

• "Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari (O'Reilly Media, 2018), especially the sections on handling missing data.
• Scikit-learn documentation on imputing missing values: https://scikit-learn.org/stable/modules/impute.html

For the feature set schema provided, the columns that need to be imputed using the most common value (mode) are typically the categorical columns. In this case,loyalty_tieris the only categorical column that should be imputed using the most common value.customer_idis a unique identifier and should not be imputed, whilespendandunitsare numerical columns that should typically be imputed using the mean or median values, not the mode.

References:

• Databricks documentation on missing value imputation: Handling Missing Data

If you need any further clarification or additional questions answered, please let me know!

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