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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
CVE is a list of publicly disclosed cybersecurity vulnerabilities that is free to search, use, and incorporate into products and services. Data can be downloaded from: Allitems [347] CVE CWE Common Weakness Enumeration data. Data can be downloaded from: Software Development Hardware Design [permanent dead link ] Research Concepts [348] CWE ...
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
This phase is called top-document retrieval and many heuristics were proposed in the literature to accelerate it, such as using a document's static quality score and tiered indexes. [7] In the second phase, a more accurate but computationally expensive machine-learned model is used to re-rank these documents.
The model is then trained on a training sample and evaluated on the testing sample. The testing sample is previously unseen by the algorithm and so represents a random sample from the joint probability distribution of x {\displaystyle x} and y {\displaystyle y} .
Information retrieval systems, such as databases and web search engines, are evaluated by many different metrics, some of which are derived from the confusion matrix, which divides results into true positives (documents correctly retrieved), true negatives (documents correctly not retrieved), false positives (documents incorrectly retrieved ...
The proposed approach improved the accuracy from 81% to 99.01% for CDI and from 75.14% to 90.17% for CRC. The use of machine learning in environmental samples has been less explored, maybe because of data complexity, especially from WGS. Some works show that it is possible to apply these tools in environmental samples.
This is because a simple model may have a high level of accuracy but too crude to be useful. For example, if the incidence of category A is dominant, being found in 99% of cases, then predicting that every case is category A will have an accuracy of 99%. Precision and recall are better measures in such cases.