QUESTION: 13

A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin.

They use the following code block to create the objective_function:

Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?

Add test set validation process
Add a random_state argument to the RandomForestRegressor operation
Remove the mean operation that is wrapping the cross_val_score operation
Replace the r2 return value with -r2
Replace the fmin operation with the fmax operation

Answer(s): D

Explanation:

When using the Hyperopt library with fmin, the goal is to find the minimum of the objective function. Since you are using cross_val_score to 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, fmin seeks to minimize the objective function, so to align with fmin's goal, you should return the negative of the R2 score (-r2). This way, by minimizing the negative R2, fmin is effectively maximizing the R2 score, which can lead to a more accurate model.
Reference
Hyperopt Documentation: http://hyperopt.github.io/hyperopt/ Scikit-Learn documentation on model evaluation: https://scikit- learn.org/stable/modules/model_evaluation.html

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QUESTION: 14

A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation.

They attempt to run the following code block, but it does not accomplish the desired task:

Which of the following changes can the data scientist make to accomplish the task?

Replace the GridSearchCV operation with RandomizedSearchCV
Replace the GridSearchCV operation with cross_validate
Replace the GridSearchCV operation with ParameterGrid
Replace the random_state=0 argument with random_state=1
Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')

Answer(s): A

Explanation:

The user wants to specify a search space for hyperparameters and let the tuning process randomly select values. GridSearchCV systematically 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.
Reference
Scikit-Learn documentation on hyperparameter tuning: https://scikit- learn.org/stable/modules/grid_search.html#randomized-parameter-optimization

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QUESTION: 15

Which of the following tools can be used to parallelize the hyperparameter tuning process for single-

node machine learning models using a Spark cluster?

MLflow Experiment Tracking
Spark ML
Autoscaling clusters
Autoscaling clusters
Delta Lake

Answer(s): B

Explanation:

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.
Reference
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 the CrossValidator and TrainValidationSplit classes, 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.
Reference
Apache Spark MLlib Documentation
Hyperparameter Tuning in Spark ML

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QUESTION: 16

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
pandas API on Spark DataFrames are more performant than Spark DataFrames
pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
pandas API on Spark DataFrames are unrelated to Spark DataFrames

Answer(s): C

Explanation:

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

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Free Databricks-Machine-Learning-Associate Exam Braindumps (page: 4)

QUESTION: 13

Explanation:

QUESTION: 14

Explanation:

QUESTION: 15

Explanation:

QUESTION: 16

Explanation: