QUESTION: 17

In a hyperparameter-based search, the hyperparameters of a model are searched based on the data on and the model's performance metrics.

TRUE
FALSE

Answer(s): A

Explanation:

In machine learning, hyperparameters are the parameters that govern the learning process and are not learned from the data. Hyperparameter optimization or hyperparameter tuning is a critical part of improving a model's performance. The goal of a hyperparameter-based search is to find the set of hyperparameters that maximizes the model's performance on a given dataset. There are different techniques for hyperparameter tuning, such as grid search, random search, and more advanced methods like Bayesian optimization. The performance of the model is assessed based on evaluation metrics (like accuracy, precision, recall, etc.), and the hyperparameters are adjusted accordingly to achieve the best performance. In Huawei's HCIA AI curriculum, hyperparameter optimization is discussed in relation to both traditional machine learning models and deep learning frameworks. The course emphasizes the importance of selecting appropriate hyperparameters and demonstrates how frameworks such as TensorFlow and Huawei's ModelArts platform can facilitate hyperparameter searches to optimize models efficiently.

Reference:

AI Overview and Machine Learning Overview: Emphasize the importance of hyperparameters in model training.
Deep Learning Overview: Highlights the role of hyperparameter tuning in neural network architectures, including tuning learning rates, batch sizes, and other key parameters. AI Development Frameworks: Discusses the use of hyperparameter search tools in platforms like TensorFlow and Huawei ModelArts.

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QUESTION: 18

The general process of building a project using machine learning involves the following steps: split data, _________________ the model, deploy the model the model, and fine-tune the model.

train

Answer(s): A

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QUESTION: 19

When feature engineering is complete, which of the following is not a step in the decision tree building process?

Decision tree generation
Pruning
Feature selection
Data cleansing

Answer(s): D

Explanation:

When building a decision tree, the steps generally involve:
Decision tree generation: This is the process where the model iteratively splits the data based on feature values to form branches.
Pruning: This step occurs post-generation, where unnecessary branches are removed to reduce overfitting and enhance generalization.
Feature selection: This is part of decision tree construction, where relevant features are selected at each node to determine how the tree branches.
Data cleansing, on the other hand, is a preprocessing step carried out before any model training begins. It involves handling missing or erroneous data to improve the quality of the dataset but is not part of the decision tree building process itself.

Reference:

Machine Learning Overview: Includes a discussion on decision tree algorithms and the process of building decision trees.
AI Development Framework: Highlights the steps for building machine learning models, separating data preprocessing (e.g., data cleansing) from model building steps.

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QUESTION: 20

Which of the following statements are true about decision trees?

The common decision tree algorithms include ID3, C4.5, and CART.
Quantitative indicators of purity can only be obtained by using information entropy.
Building a decision tree means selecting feature attributes and determining their tree structure.
A key step to building a decision tree involves dividing all feature attributes and comparing the purity of the division's result sets.

Answer(s): A,C,D

Explanation:

A: TRUE. The common decision tree algorithms include ID3, C4.5, and CART. These are the most widely used algorithms for decision tree generation.
B: FALSE. Purity in decision trees can be measured using multiple metrics, such as information gain, Gini index, and others, not just information entropy. C. TRUE. Building a decision tree involves selecting the best features and determining their order in the tree structure to split the data effectively.
D: TRUE. One key step in decision tree generation is evaluating the purity of different splits (e.g., how well the split segregates the target variable) by comparing metrics like information gain or Gini index.

Reference:

Machine Learning Overview: Covers decision tree algorithms and their use cases. Deep Learning Overview: While this focuses on neural networks, it touches on how decision-making algorithms are used in structured data models.

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Free H13-311_V3.5 Exam Braindumps (page: 6)

QUESTION: 17

Explanation:

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QUESTION: 18

QUESTION: 19

Explanation:

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QUESTION: 20

Explanation:

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