Sökning: "Supervised Learn- ing Models"

Hittade 3 uppsatser innehållade orden Supervised Learn- ing Models.

  1. 1. Analyzing the performance of active learning strategies on machine learning problems

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

    Författare :Vendela Werner; [2023]
    Nyckelord :computer science; bioinformatics; machine learning; active learning; artificial intelligence; supervised learning; Astrazeneca; maskininlärning; artificiell intelligens; datorvetenskap; active learning; bioinformatik; supervised learning;

    Sammanfattning : Digitalisation within industries is rapidly advancing and data possibilities are growing daily. Machine learning models need a large amount of data that are well-annotated for good performance. To get well-annotated data, an expert is needed, which is expensive, and the annotation itself could be very time-consuming. LÄS MER

  3. 2. Employee Turnover Prediction - A Comparative Study of Supervised Machine Learning Models

    Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Författare :Suvoj Reddy Kovvuri; Lydia Sri Divya Dommeti; [2022]
    Nyckelord :Machine Learning; Employee Turnover Prediction; Supervised Learn- ing Models; Logistic Regression; Naive Bayes Classifier; Random Forest Classifier; XGBoost;

    Sammanfattning : Background: In every organization, employees are an essential resource. For several reasons, employees are neglected by the organizations, which leads to employee turnover. Employee turnover causes considerable losses to the organization. LÄS MER

  4. 3. Utilizing unlabeled data in cell type identification : A semi-supervised learning approach to classification

    Master-uppsats, Linköpings universitet/Statistik och maskininlärning

    Författare :Thijs Quast; [2020]
    Nyckelord :Semi-supervised; cell type identification; scRNA-seq;

    Sammanfattning : Recent research in bioinformatics has presented multiple cell type identification meth- dologies using single cell RNA sequence data (scRNA-seq). However, a consensus on which cell typing methodology consistently demonstrates superior performance remains absent. LÄS MER