Therefore, random forest regression is a very effective method for health insurance prediction. The next model is the linear regression model with a model score of 0.7584.

David Muchlinski, David Siroky, Jingrui He, Matthew Kocher, Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data, Political Analysis, Vol. 24, No. 1 ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

This article compares the two approaches (linear model on the one hand and two versions of random forests on the other hand) and finds both striking similarities and differences, some of which can be ...

Using a random forest regression model, they have identified the properties that affect protein adsorption and cell adhesion onto these films, providing a guideline for the development of anti ...

In this post, we’ll discuss some of the differences between fixed and random effects models when applied to panel data — that is, data collected over time on the same unit of analysis — and how these ...

Recent study focused on predicting short birth intervals (defined as less than 33 months) among reproductive-age women in ...