Sökning: "Interpretable Machine Learning"
Visar resultat 1 - 5 av 23 avhandlingar innehållade orden Interpretable Machine Learning.
1. Applied Machine Learning in Steel Process Engineering : Using Supervised Machine Learning Models to Predict the Electrical Energy Consumption of Electric Arc Furnaces
Sammanfattning : The steel industry is in constant need of improving its production processes. This is partly due to increasing competition and partly due to environmental concerns. One commonly used method for improving these processes is through the act of modeling. LÄS MER
2. Interpretable machine learning models for predicting with missing values
Sammanfattning : Machine learning models are often used in situations where model inputs are missing either during training or at the time of prediction. If missing values are not handled appropriately, they can lead to increased bias or to models that are not applicable in practice without imputing the values of the unobserved variables. LÄS MER
3. Patterns in big data bioinformatics : Understanding complex diseases with interpretable machine learning
Sammanfattning : Alterations in the flow of genetic information may lead to complex diseases. Such changes are measured with various omics techniques that usually produce the so-called “big data”. Using interpretable machine learning (ML), we retrieved patterns from transcriptomics data sets. LÄS MER
4. Machine learning applications in healthcare
Sammanfattning : Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself. A technology that can be useful in an environment as data-intensive as healthcare is machine learning. LÄS MER
5. Elucidation of complex diseases by machine learning
Sammanfattning : Uncovering the interpretability of models for complex health-related problems is a crucial task that is often neglected in machine learning (ML). The amount of available data makes the problem even more complicated. LÄS MER