Essays about: "interpretability"
Showing result 1 - 5 of 114 essays containing the word interpretability.
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1. Feature Selection for Microarray Data via Stochastic Approximation
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : This thesis explores the challenge of feature selection (FS) in machine learning, which involves reducing the dimensionality of data. The selection of a relevant subset of features from a larger pool has demonstrated its effectiveness in enhancing the performance of various machine learning algorithms. READ MORE
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2. The Power of Credit Scoring: Evaluating Machine Learning and Traditional Models in Swedish Retail Banking
University essay from Göteborgs universitet/Graduate SchoolAbstract : In this paper, we investigate and compare different credit scoring models, with special attention paid to machine learning approaches outperforming traditional models. We explore a recently proposed method called the PLTR model, which is a combination of machine learning and traditional logistic regression. READ MORE
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3. Neural Network-based Anomaly Detection Models and Interpretability Methods for Multivariate Time Series Data
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Anomaly detection plays a crucial role in various domains, such as transportation, cybersecurity, and industrial monitoring, where the timely identification of unusual patterns or outliers is of utmost importance. Traditional statistical techniques have limitations in handling complex and highdimensional data, which motivates the use of deep learning approaches. READ MORE
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4. Generating an Interpretable Ranking Model: Exploring the Power of Local Model-Agnostic Interpretability for Ranking Analysis
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Machine learning has revolutionized recommendation systems by employing ranking models for personalized item suggestions. However, the complexity of learning-to-rank (LTR) models poses challenges in understanding the underlying reasons contributing to the ranking outcomes. READ MORE
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5. A Dual-Lens Approach to Loss Given Default Estimation: Traditional Methods and Variable Analysis
University essay from KTH/Matematik (Avd.)Abstract : This report seeks to thoroughly examine different approaches to estimating Loss Given Default through a comparison of traditional estimation methods, as well as a deeper variable analysis on micro, small, and medium-sized companies using primarily regression decision trees. The comparative study concluded that estimating loss given default depends heavily on business-specific factors and data variety. READ MORE
