Sökning: "Forest insurance"

Visar resultat 11 - 15 av 31 uppsatser innehållade orden Forest insurance.

  1. 11. Evaluating Random Forest and a Long Short-Term Memory in Classifying a Given Sentence as a Question or Non-Question

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Fredrik Ankaräng; Fabian Waldner; [2019]
    Nyckelord :Bag-of-Words; Chatbot; Classification; LSTM; Machine Learning; Natural Language Processing; Random Forest; Word2Vec;

    Sammanfattning : Natural language processing and text classification are topics of much discussion among researchers of machine learning. Contributions in the form of new methods and models are presented on a yearly basis. However, less focus is aimed at comparing models, especially comparing models that are less complex to state-of-the-art models. LÄS MER

  3. 12. Uplift Modeling : Identifying Optimal Treatment Group Allocation and Whom to Contact to Maximize Return on Investment

    Master-uppsats, Linköpings universitet/Statistik och maskininlärning

    Författare :Henrik Karlsson; [2019]
    Nyckelord :Causal Effect; Uplift Modeling; Class Transformation Method; Model Uplift Directly; Random Forest; XGBoost; Qini Curve; Qini Coefficient; Optimal Control Group Allocation;

    Sammanfattning : This report investigates the possibilities to model the causal effect of treatment within the insurance domain to increase return on investment of sales through telemarketing. In order to capture the causal effect, two or more subgroups are required where one group receives control treatment. LÄS MER

  4. 13. Rating corrumption within insurance companies using Bayesian network classifiers

    Magister-uppsats, Umeå universitet/Statistik

    Författare :Oscar Öhman; [2019]
    Nyckelord :;

    Sammanfattning : Bayesian Network (BN) classifiers are a type of probabilistic models. The learning process consists of two steps, structure learning and parameter learning. Four BN classifiers will be learned. These are two different Naive Bayes classifiers (NB), one Tree Augmented Naive Bayes classifier (TAN) and one Forest Naive Bayes classifier (FAN). LÄS MER

  5. 14. LSTM vs Random Forest for Binary Classification of Insurance Related Text

    Kandidat-uppsats, KTH/Matematisk statistik

    Författare :Hannes Kindbom; [2019]
    Nyckelord :Random Forest; Classification; Natural Language Processing; Machine Learning; Neural Networks; Bag of Words; Bachelor Thesis; Diffusion of Innovation; Adoption Rate; User Experience; Random Forest; Klassificering; Språkteknologi; Maskininlärning; Neurala nätverk; Bag of Words; Kandidatexamensarbete; Användarupplevelse;

    Sammanfattning : The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. LÄS MER

  7. 15. Kontaktstrategi för en skogsägarförening : en benchmarking studie om kundkontaktmellan en skogsägarförening och dess medlemmar

    Kandidat-uppsats, SLU/Department of Forest Biomaterials and Technology (from 131204)

    Författare :Carolina Berg Rustas; Magnus Persson; [2017]
    Nyckelord :säljhinder; kontaktskapande; kundnöjdhet; arbetsprocesser; kommunikation;

    Sammanfattning : Skogsägarföreningen har identifierat att ett av de största problemen för att uppnå mål kopplade till medlemsnytta ligger i förmågan att komma i kontakt med sina medlemmar. Med bakgrund av att skogsägarstrukturen förändrats under de senaste åren gjordes denna studie och genom en benchmarkingstudie undersöka vilka skillnader som finns mellan Skogsägareföreningens kontaktstrategi och kontaktstrategier hos företag i andra branscher med dokumenterat hög kundnöjdhet. LÄS MER