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

Page 4 of 16
PDF with 60 Questions

An algorithm of unsupervised learning classifies samples in a dataset into several categories. Samples belonging to the same category have high similarity.

  1. TRUE
  2. FALSE

Answer(s): A

Explanation:

In unsupervised learning, the goal is to find hidden patterns or intrinsic structures in input data without labeled outcomes. One common unsupervised learning task is clustering, where an algorithm groups the dataset into several categories or clusters. Samples within the same cluster have high similarity based on certain features, while samples in different clusters have low similarity. Examples of clustering algorithms include k-means and hierarchical clustering.


Reference:

Huawei HCIA-AI Certification, Machine Learning Overview.



Which of the following statements is false about the debugging and application of a regression model?

  1. If the model does not meet expectations, you need to use data cleansing and feature engineering.
  2. After model training is complete, you need to use the test dataset to evaluate your model so that its generalization capability meets expectations.
  3. If overfitting occurs, you can add a regularization term to the Lasso or ridge regression and adjust hyperparameters.
  4. If underfitting occurs, you can use a more complex regression model, for example, logistic regression.

Answer(s): D

Explanation:

Logistic regression is not a solution for underfitting in regression models, as it is used primarily for classification problems rather than regression tasks. If underfitting occurs, it means that the model is too simple to capture the underlying patterns in the data. Solutions include using a more complex regression model like polynomial regression or increasing the number of features in the dataset. Other options like adding a regularization term for overfitting (Lasso or Ridge) and using data cleansing and feature engineering are correct methods for improving model performance.


Reference:

Huawei HCIA-AI Certification, AI Model Debugging and Optimization.



In machine learning, which of the following inputs is required for model training and prediction?

  1. Neural network
  2. Historical data
  3. Training algorithm
  4. Manual program

Answer(s): B

Explanation:

In machine learning, historical data is crucial for model training and prediction. The model learns from this data, identifying patterns and relationships between features and target variables.
While the training algorithm is necessary for defining how the model learns, the input required for the model is historical data, as it serves as the foundation for training the model to make future predictions.
Neural networks and training algorithms are parts of the model development process, but they are not the actual input for model training.


Reference:

Huawei HCIA-AI Certification, Machine Learning Workflow.



Which of the following statements about datasets are true?

  1. Testing refers to a process that uses a trained model for prediction. The dataset, which is used for testing, is called a testing set, and each sample is called a test sample.
  2. A dataset generally has multiple dimensions. In each dimension, events or attributes that reflect the performance or nature of a sample in a particular aspect are called features.
  3. In machine learning, a dataset is generally divided into a training set, validation set, and test set.
  4. When it comes to the machine learning process, the validation set and the test set are essentially the same.

Answer(s): A,B,C

Explanation:

In machine learning:
The testing set is a dataset used after training to evaluate the model's performance and generalization ability. Each sample in this set is called a test sample. A dataset generally has multiple dimensions, with each dimension representing a feature or attribute of the data.
A typical machine learning process divides the data into a training set (to train the model), a validation set (to tune hyperparameters and avoid overfitting), and a test set (to evaluate the model's final performance).
The statement that the validation set and test set are the same is false because they serve different purposes: validation is for hyperparameter tuning, while testing is for final model evaluation.


Reference:

Huawei HCIA-AI Certification, Machine Learning Data and Evaluation.






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