HCIA-AI H13-311_V3.5 Passing Score

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.

Artificial intelligence is a broad field that encompasses several subfields. Two key subfields are:

Expert systems, which are computer programs that mimic the decision-making abilities of a human expert by reasoning through bodies of knowledge. These systems are used in various domains such as healthcare, engineering, and finance.

Computer vision, which enables machines to interpret and understand visual data from the world. It includes tasks such as object detection, image recognition, and video analysis.

While options like backpropagation and smart finance are related to AI, they represent specific algorithms or application areas rather than broad subfields.

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.

HCIA AI References:

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.

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.