AI agents don't just need to guess what might come next; they need to choose what to do, and discernment, or phronesis, is ...

This paper presents a general framework based on copulas for modeling dependent multivariate uncertainties through the use of a decision tree. The proposed dependent decision tree model allows ...

The two main downsides to decision trees are that they often don't work well with large datasets, and they are highly susceptible to model overfitting. When tackling a binary classification problem, ...

A decision tree analytical approach has been proposed, which incorporates quality of life outcomes and survival data to quantitatively determine the optimal approach for individual patients with ...

Decision trees, regression, and neural networks all are types of predictive models. People often confuse predictive analytics with machine learning even though the two are different disciplines.

I used this dataset to train a decision tree model to predict a batter’s swing or non-swing. Decision trees are a non-linear model that takes each variable and tries to use it to split the dataset.

Conclusions Decision tree analysis yielded a statistically significant decision tree model which can be used clinically to identify patients at initial presentation who are at a higher risk of having ...