Sökning: "Random Forest Regression"
Visar resultat 1 - 5 av 71 uppsatser innehållade orden Random Forest Regression.
1. Predicting rifle shooting accuracy from context and sensor data : A study of how to perform data mining and knowledge discovery in the target shooting domainKandidat-uppsats, Högskolan i Jönköping/JTH, Datateknik och informatik; Högskolan i Jönköping/JTH, Datateknik och informatik
Sammanfattning : The purpose of this thesis is to develop an interpretable model that gives predictions for what factors impacted a shooter’s results. Experiment is our chosen research method. Our three independent variables are weapon movement, trigger pull force and heart rate. Our dependent variable is shooting accuracy. LÄS MER
- Master-uppsats, KTH/Matematisk statistik; KTH/Matematisk statistik
Sammanfattning : The interest in modeling non-maturing deposits has skyrocketed ever since thefinancial crisis 2008. Not only from a regulatory and legislative perspective,but also from an investment and funding perspective.Modeling of non-maturing deposits is a very broad subject. LÄS MER
3. Customer Churn Prediction for PC Games : Probability of churn predicted for big-spenders usingsupervised machine learningMaster-uppsats, KTH/Optimeringslära och systemteori
Sammanfattning : Paradox Interactive is a Swedish video game developer and publisher which has players all around the world. Paradox’s largest platform in terms of amount of players and revenue is the PC. LÄS MER
- Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)
Sammanfattning : It is well established that designing good heuristics for solving Constraint Programming models requires years of domain experience and a huge amount of trial and error. In this thesis project, we conduct an empirical study of whether Machine Learning and Deep Learning techniques have the potential to help the design of constraint solving heuristics. LÄS MER
- Kandidat-uppsats, Uppsala universitet/Statistiska institutionen
Sammanfattning : This paper uses statistical learning to examine and compare three different statistical methods with the aim to predict credit card fraud. The methods compared are Logistic Regression, K-Nearest Neighbour and Random Forest. LÄS MER