Logistic regression is an approach to supervised machine learning that models selected values to predict possible outcomes. In this course, Notre Dame professor Frederick Nwanganga provides you with a ...

The purpose of this study is to identify the socioeconomic and demographic factors associated with household food insecurity in Namibia by fitting an ordinal logistic regression model using the ...

Consequently, the use of ordinal logistic regression offers a practical and effective means of determining pedestrian safety factors. Better pedestrian facilities should be constructed by considering ...

3. The Approach The development of the logistics engineering - regression based predictive feature model using sk-learn for vehicle performance in logistics clusters must begin with engineering as ...

Training a Logistic Regression Model Computing a prediction is simple, but where do the model weights and the bias come from? As it turns out, there are different underlying theoretical models, and ...

Next, the demo trains a logistic regression model using raw Python, rather than by using a machine learning code library such as Microsoft ML.NET or scikit. [Click on image for larger view.] Figure 1: ...

RoBERTa (BERT) vs Logistic Regression Overview This project will be showcasing the steps to build two different emotion detection NLP models (using RoBERTa and Logistic Regression, as the title ...

The main purpose of this paper is to investigate the happiness factors and assess the performance of machine learning techniques on predicting the happiness levels of European immigrants and natives.