Here's the complete project structure: decision-trees-random-forest_from_scratch_probabilistic-classifier/ │ ├── data/ │ ├── raw/ │ └── processed/ │ ├── src/ │ ├── utils/ │ │ ├── data_split.py │ │ ├── ...

Abstract: Multi-class classification presents a significant challenge in supervised machine learning, and it is frequently applied across various real-world domains. Random Forest (RF) stands out as a ...

Artur is a copywriter and SEO specialist, as well as a small business owner. In his free time, he loves to play computer games and is glad that he was able to connect his professional career with his ...

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior ...

ABSTRACT: In this study, multi-source remote sensing data and machine learning algorithms were used to delineate the prospect area of remote sensing geological prospecting in eastern Botswana. Landsat ...

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

Objectives: This study proposes to construct a model to replace the on-road test and provide a bundled assessment on the driving function of stroke patients. Methods: Clinical data were collected from ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random forest regression technique (and a variant called bagging regression), where the goal is to ...

ABSTRACT: Over the past ten years, there has been an increase in cardiovascular disease, one of the most dangerous types of disease. However, cardiovascular detection is a technique that analyzes data ...