multinomial-logistic-regression

There are 47 repositories under multinomial-logistic-regression topic.

  • PGR-IESB-P9040-CDNA-20203

    Métodos Estatísticos de Apoio à Decisão II

    Language:Jupyter Notebook
  • Cardiotocographic-Monitoring-and-Classification-of-Fetal-Outcome

    Modeling the relationship between fetal heart rate signals and fetal outcome using multinomial logistic regression

  • Fake-News-Classification

    Given the title of a fake news article A and the title of a coming news article B, program classifies B into agree, disagree, and unrelated.

    Language:HTML
  • Satellite-Images-Prediction-with-Random-Forests-and-Multinomial-Logistic-Regression

    Satellite-Images-Prediction-with-Random-Forests-and-Multinomial-Logistic-Regression

    Developed a random forest model to predict satellite images with 91.4% accuracy and 0.02% standard deviation of accuracies for all replications.

  • fire_incidents_investigation_and_modelling

    An investigation of San Francisco Fire Incidents using open data - exploratory analysis and modelling logit and multinomial logit regressions

    Language:Jupyter Notebook
  • Machine-learning-techniques-for-recognising-handwritten-digits

    This project encompasses a range of neural and non-neural model implementations to classifiy MNIST digits. The goal is to compare the performance of each technique including details of hyper-parameters, training ans testing errors, training and testing duration and additional parameters used in the analysis.

    Language:Jupyter Notebook
  • NLP-SentimentAnalysis

    This program uses the polarity and intensity of the words to assign one of five ratings to product reviews using Multinomial Logistic Regression. The data given was biased with more positive than negative reviews and hence required regularization.

    Language:Jupyter Notebook
  • AcceleratedCVonMLR_matlab

    This MATLAB package enables to efficiently compute leave-one-out cross validation error for multinomial logistic regression with elastic net (L1 and L2) penalty. The computation is based on an analytical approximation, which enables to avoid re-optimization and to reduce much computational time. Python version: https://github.com/T-Obuchi/AcceleratedCVonMLR_python

    Language:MATLAB