One example of a classification problem is identifying an email ... Similarly, for clustering based on the available data set, algorithms such as k-means, hierarchical clustering, and density based ...

For example, clustering customers based on preferences doesn't require ... The job of the classification algorithm is to discover how that set of attributes reaches its conclusion.

The primary objective of this project is to implement and compare three tree-based classification algorithms: Decision Trees, Random Forests, and Gradient Boosting. The project aims to provide a ...

The Image Classification Examples repo contains several examples of image classification algorithms for use with image files. Examples can be found in the python directory. If you're using Docker, ...

Abstract: k-nearest neighbor and centroid-based classification algorithms are frequently used in text classification due to their simplicity and performance. While k-nearest neighbor algorithm usually ...

when using various well known decision tree based packet classification algorithms. Worse, two similar rule sets, in terms of types and number of rules, can give rise to widely differing performance ...

The classification algorithm requires nine machine ... The confusion matrix is shown as an example in Table 2. In this paper, three feature selection methods are used, the feature selection method ...

For example, Li et al. (2011 ... Flowchart of electroencephalography (EEG) joint feature classification algorithm based on instance transfer and ensemble learning. (1) In the data preprocessing part, ...