For the random forest, we used twice the maximum to give missing values a distinct meaning that would allow the model to leverage this information. Lastly, we used the "Synthetic Minority Oversampling Technique" (SMOTE) to create artificial examples for the minority class in the training data69...
Then we forget about the training set and evaluate how each model performs on the testing data. And here’s when things get changed. The linear model makes only one mistake while the higher-degree polynomial model fails two times, meaning the former does better. And you wouldn’t find that...
An e-learning platform developing an AI tutor that explains complex concepts in simpler terms uses METEOR to evaluate the quality of its explanations. This metric helps assess whether the AI-generated explanations effectively convey the same meaning as expert-written materials, even if using different...
Each cluster of the clusters represents a distinct semantic meaning of the sampled model outputs. The method also includes generating a confidence metric for the user input. The confidence metric includes a predictive entropy of the clusters. The method also includes routing the user input based on...
Clearly, the dot product calculation is straightforward (the simplest of the three) — and this gives us benefits in terms of computation time. However, there is one drawback. It is not normalized — meaning larger vectors will tend to score higher dot products, despite being less similar. ...
A similarity score (ranging from 0% meaning no match to 100% meaning a perfect match) is used to make the determination of a match or a non-match decision. A false positive occurs when the solution considers images of two different individuals to be the same person. A false negative, ...
To understand topics, they mentioned the necessity to categorize the entities in a given corpus. South et al. (2020) [9] proposed DebateVis to analyze debate for non-expert users. They suggested agree, attack, defense, and neutral reference as factors for comparing the meaning of dialogue ...
BLEU relies on n-gram matching, where it counts the number of overlapping n-grams between the machine-generated translation and the reference translation. However, this approach has several shortcomings. For instance, BLEU does not consider the stems and synonyms of words, meaning that it does ...
Some classifiers are trained using a probabilistic framework, such as maximum likelihood estimation, meaning that their probabilities are already calibrated. An example would be logistic regression. Many nonlinear classifiers are not trained under a probabilistic framework and therefore require their probabili...
Delivery-performance metrics that have no dependencies on software development methods are useful for monitoring the effectiveness of process-improvement efforts because they have the same meaning regardless of how the work is carried out. On the other hand, metrics that depend on development approach,...