Releases: parrt/random-forest-importances
Releases · parrt/random-forest-importances
Improve compatibility with 0.24 scikit-learn
1.3.6 Add title to plots, improve sklearn compatibility, add support for different correlation methods
add method argument to the correlation methods.
small bug fix for 0.22 sklearn
Merge pull request #35 from parrt/improve-oob-sklearn-compatibility Improve #32 that fixed #27
Improve bar charts for feature importances, add feature dependence heatmap
- Added
plot_dependence_heatmap()
to plot feature dependence heat maps - Improve feature importance plots so that the bars are always the same. You can specify a title and there is better scaling support.
- The plotting routines return
PimpViz
objects that by default render the current matplotlib image via SVG, getting a much sharper image than the default PNG. - dropcol importance was relying on OOB scores instead of the more general model scoring/metric.
- Added a stemplot version that mimics the bar chart for feature importance.
- Added precision argument to the correlation heat map function.
- Rebuilt the notebook examples and the ones that generate images for the paper.
- Added a section to the paper that shows the feature dependence heat map applied to the breast-cancer data set.
Improve bar charts for feature importances
1.2.2 improve importance plots so bars are typically same width across plot…