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NEW QUESTION # 46
A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFrame features_df. A list of the names of the string columns is assigned to the input_columns variable.
They have developed this code block to accomplish this task:
The code block is returning an error.
Which of the following adjustments does the data scientist need to make to accomplish this task?
Answer: A
Explanation:
The OneHotEncoder in Spark ML requires numerical indices as inputs rather than string labels. Therefore, you need to first convert the string columns to numerical indices using StringIndexer. After that, you can apply OneHotEncoder to these indices.
Corrected code:
from pyspark.ml.feature import StringIndexer, OneHotEncoder # Convert string column to index indexers = [StringIndexer(inputCol=col, outputCol=col+"_index") for col in input_columns] indexer_model = Pipeline(stages=indexers).fit(features_df) indexed_features_df = indexer_model.transform(features_df) # One-hot encode the indexed columns ohe = OneHotEncoder(inputCols=[col+"_index" for col in input_columns], outputCols=output_columns) ohe_model = ohe.fit(indexed_features_df) ohe_features_df = ohe_model.transform(indexed_features_df) Reference:
PySpark ML Documentation
NEW QUESTION # 47
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?
Answer: 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
NEW QUESTION # 48
Which statement describes a Spark ML transformer?
Answer: C
Explanation:
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 like StringIndexer, VectorAssembler, and StandardScaler.
Reference:
Databricks documentation on transformers: Transformers in Spark ML
NEW QUESTION # 49
The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?
Answer: C
Explanation:
For large datasets, Spark ML uses iterative optimization methods to distribute the training of a linear regression model. Specifically, Spark MLlib employs techniques like Stochastic Gradient Descent (SGD) and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization to iteratively update the model parameters. These methods are well-suited for distributed computing environments because they can handle large-scale data efficiently by processing mini-batches of data and updating the model incrementally.
Reference:
Databricks documentation on linear regression: Linear Regression in Spark ML
NEW QUESTION # 50
A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference.
In which situation will the machine learning engineer be correct?
Answer: E
Explanation:
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.
Reference:
Model Ensemble Techniques
NEW QUESTION # 51
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