Which of the following algorithms presents the most chaotic landscape on the loss surface?
Answer(s): A
Stochastic Gradient Descent (SGD) presents the most chaotic landscape on the loss surface because it updates the model parameters for each individual training example, which can introduce a significant amount of noise into the optimization process. This leads to a less smooth and more chaotic path toward the global minimum compared to methods like batch gradient descent or mini-batch gradient descent, which provide more stable updates.
Huawei HCIA-AI Certification, Machine Learning Algorithms.
Which of the following statements are true about the k-nearest neighbors (k-NN) algorithm?
Answer(s): B,D
The k-nearest neighbors (k-NN) algorithm is a non-parametric algorithm used for both classification and regression. In classification tasks, it typically uses majority voting to assign a label to a new instance based on the most common class among its nearest neighbors. The algorithm works by calculating the distance (often using Euclidean distance) between the query point and the points in the dataset, and then assigning the query point to the class that is most frequent among its k nearest neighbors.For regression tasks, k-NN can predict the outcome based on the mean of the values of the k nearest neighbors, although this is less common than its classification use.
Huawei HCIA-AI Certification, Machine Learning Overview.
An algorithm of unsupervised learning classifies samples in a dataset into several categories. Samples belonging to the same category have high similarity.
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.
Which of the following statements is false about the debugging and application of a regression model?
Answer(s): D
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.
Huawei HCIA-AI Certification, AI Model Debugging and Optimization.
In machine learning, which of the following inputs is required for model training and prediction?
Answer(s): B
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.
Huawei HCIA-AI Certification, Machine Learning Workflow.
Post your Comments and Discuss Huawei H13-311_V3.5 exam prep with other Community members:
Our website is free, but we have to fight against AI bots and content theft. We're sorry for the inconvenience caused by these security measures. You can access the rest of the H13-311_V3.5 content, but please register or login to continue.