Sökning: "Forest insurance"
Visar resultat 1 - 5 av 22 uppsatser innehållade orden Forest insurance.
- Kandidat-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik
Sammanfattning : Problems with extreme weather, such as drought, has increased due to greenhouse emissions. As one of the consequences to this, forest fires have emerged more frequently on a global level. This became obvious in Sweden, in the summer of 2018, which has been stated to have been the worst affected year of forest fires in modern time. LÄS MER
2. Evaluating Random Forest and a Long Short-Term Memory in Classifying a Given Sentence as a Question or Non-QuestionKandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS); KTH/Skolan för elektroteknik och datavetenskap (EECS)
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. Uplift Modeling : Identifying Optimal Treatment Group Allocation and Whom to Contact to Maximize Return on InvestmentMaster-uppsats, Linköpings universitet/Statistik och maskininlärning
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
- Magister-uppsats, Umeå universitet/Statistik
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
- Kandidat-uppsats, KTH/Matematisk statistik
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