Databricks Databricks-Machine-Learning-Associate Exam With Confidence Using Practice Dumps

To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:

mlflow.register_model(f"runs:/{run_id}/model","best_model")

This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.

References

• MLflow documentation on model registry: https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model

Spark ML (part of Apache Spark's MLlib) is designed to handle machine learning tasks across multiple nodes in a cluster, effectively parallelizing tasks like hyperparameter tuning. It supports various machine learning algorithms that can be optimized over a Spark cluster, making it suitable for parallelizing hyperparameter tuning for single-node machine learning models when they are adapted to run on Spark.

References

• Apache Spark MLlib Guide:https://spark.apache.org/docs/latest/ml-guide.html

Spark ML is a library within Apache Spark designed for scalable machine learning. It provides tools to handle large-scale machine learning tasks, including parallelizing the hyperparameter tuning process for single-node machine learning models using a Spark cluster. Here’s a detailed explanation of how Spark ML can be used:

• Hyperparameter Tuning with CrossValidator: Spark ML includes theCrossValidatorandTrainValidationSplitclasses, which are used for hyperparameter tuning. These classes can evaluate multiple sets of hyperparameters in parallel using a Spark cluster.

from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

from pyspark.ml.evaluation import BinaryClassificationEvaluator

# Define the model

model = ...

# Create a parameter grid

paramGrid = ParamGridBuilder() \

addGrid(model.hyperparam1, [value1, value2]) \

addGrid(model.hyperparam2, [value3, value4]) \

build()

# Define the evaluator

evaluator = BinaryClassificationEvaluator()

# Define the CrossValidator

crossval = CrossValidator(estimator=model,

estimatorParamMaps=paramGrid,

evaluator=evaluator,

numFolds=3)

• Parallel Execution: Spark distributes the tasks of training models with different hyperparameters across the cluster’s nodes. Each node processes a subset of the parameter grid, which allows multiple models to be trained simultaneously.
• Scalability: Spark ML leverages the distributed computing capabilities of Spark. This allows for efficient processing of large datasets and training of models across many nodes, which speeds up the hyperparameter tuning process significantly compared to single-node computations.

References

• Apache Spark MLlib Documentation
• Hyperparameter Tuning in Spark ML

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