Understanding Applied Machine Learning 2019 Lecture 05 Preprocessing
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Key Takeaways about Applied Machine Learning 2019 Lecture 05 Preprocessing
- Basic principles of data visualization, introduction to matplotlib Also check out this amazing free book: ...
- Feature importance measures, partial dependence plots. Univariate and multivariate feature selection, recursive feature selection.
- Metrics for binary classification, multiclass and regression. ROC curves, precision-recall curves. Class website with slides and ...
- Course materials at https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/
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Detailed Analysis of Applied Machine Learning 2019 Lecture 05 Preprocessing
Class materials at https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/ Nearest neighbors, nearest centroids, cross-validation and grid-search Materials on the course website: ... Logistic Regression, linear SVMs, the kernel trick One-vs-Rest and One-vs-One multi-class strategies. Class website with slides ...
Decision trees for classification and regression, tree pre-pruning, bagging and ensembles, random forests, extremely randomized ...
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