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Predictive Modeling with Supervised Machine Learning
| Content Provider | Scilit |
|---|---|
| Author | Akalin, Altuna |
| Copyright Year | 2020 |
| Description | This chapter introduces the supervised machine learning applications for predictive modeling. Cross-validation works by splitting the data into randomly sampled k subsets, called k-folds. Machine learning and statistics are related and sometimes overlapping fields. Statistical inference is the main purpose of statistics. The aim of inference is to find statistical properties of the underlying data and to estimate the uncertainty about those properties. One of the easiest things to wrap our heads around when we are trying to predict a label such as disease subtype is to look for similar samples and assign the labels of those similar samples to our sample. One important and popular metric when evaluating performance is looking at receiver operating characteristic (ROC) curves. The ROC curve is created by evaluating the class probabilities for the model across a continuum of thresholds. ROC curves can also be used to determine alternate cutoffs for class probabilities for two-class problems. Book Name: Computational Genomics with R |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2016-0-96920-2&isbn=9780429084317&format=googlePreviewPdf |
| Ending Page | 202 |
| Page Count | 56 |
| Starting Page | 147 |
| DOI | 10.1201/9780429084317-5 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-12-16 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Computational Genomics with R Artificial Intelligence Modeling Statistics Machine Learning Inference Roc Supervised Curves Probabilities Looking |
| Content Type | Text |
| Resource Type | Chapter |
