diff --git a/README.md b/README.md index a5377a048..8bade55c1 100644 --- a/README.md +++ b/README.md @@ -143,6 +143,4 @@ according to the terms and conditions of the Creative Commons Attribution 4.0 In ## Contact -I received a lot of feedback and questions about mlxtend recently, and I thought that it would be worthwhile to set up a public communication channel. Before you write an email with a question about mlxtend, please consider posting it here since it can also be useful to others! Please join the [Google Groups Mailing List](https://groups.google.com/forum/#!forum/mlxtend)! - -If Google Groups is not for you, please feel free to write me an [email](mailto:mail@sebastianraschka.com) or consider filing an issue on [GitHub's issue tracker](https://github.com/rasbt/mlxtend/issues) for new feature requests or bug reports. In addition, I setup a [Gitter channel](https://gitter.im/rasbt/mlxtend?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) for live discussions. +The best way to ask questions is via the [GitHub Discussions channel](https://github.com/rasbt/mlxtend/discussions). In case you encounter usage bugs, please don't hesitate to use the [GitHub's issue tracker](https://github.com/rasbt/mlxtend/issues) directly. diff --git a/docs/sources/CHANGELOG.md b/docs/sources/CHANGELOG.md index ae436f964..66d048dd2 100755 --- a/docs/sources/CHANGELOG.md +++ b/docs/sources/CHANGELOG.md @@ -7,11 +7,15 @@ The CHANGELOG for the current development version is available at --- -### Version 0.18.1 (TBD) +### Version 0.19.0 (09/02/2021) ##### Downloads -- - +- [Source code (zip)](https://github.com/rasbt/mlxtend/archive/v0.19.0.zip) + +- [Source code (tar.gz)](https://github.com/rasbt/mlxtend/archive/v0.19.0.tar.gz) + + ##### New Features diff --git a/docs/sources/user_guide/evaluate/bootstrap_point632_score.ipynb b/docs/sources/user_guide/evaluate/bootstrap_point632_score.ipynb index 9f2f66b46..e21417d1a 100644 --- a/docs/sources/user_guide/evaluate/bootstrap_point632_score.ipynb +++ b/docs/sources/user_guide/evaluate/bootstrap_point632_score.ipynb @@ -54,7 +54,7 @@ "\n", "Now, to address the bias that is due to this the sampling with replacement, Bradley Efron proposed the *.632 Estimate* that we mentioned earlier, which is computed via the following equation:\n", "\n", - "$$\\text{ACC}_{boot} = \\frac{1}{b} \\sum_{i=1}^b \\big(0.632 \\cdot \\text{ACC}_{h, i} + 0.368 \\cdot \\text{ACC}_{r, i}\\big), $$\n", + "$$\\text{ACC}_{boot} = \\frac{1}{b} \\sum_{i=1}^b \\big(0.632 \\cdot \\text{ACC}_{h, i} + 0.368 \\cdot \\text{ACC}_{train}\\big), $$\n", "\n", "where $\\text{ACC}_{train}$ is the accuracy computed on the whole training set, and $\\text{ACC}_{h, i}$ is the accuracy on the out-of-bag sample.\n", "\n", @@ -63,7 +63,7 @@ "Now, while the *.632 Boostrap* attempts to address the pessimistic bias of the estimate, an optimistic bias may occur with models that tend to overfit so that Bradley Efron and Robert Tibshirani proposed the *The .632+ Bootstrap Method* (Efron and Tibshirani, 1997). Instead of using a fixed \"weight\" $\\omega = 0.632$ in\n", "\n", "$$\n", - "ACC_{\\text{boot}} = \\frac{1}{b} \\sum_{i=1}^b \\big(\\omega \\cdot \\text{ACC}_{h, i} + (1-\\omega) \\cdot \\text{ACC}_{r, i} \\big), $$\n", + "ACC_{\\text{boot}} = \\frac{1}{b} \\sum_{i=1}^b \\big(\\omega \\cdot \\text{ACC}_{h, i} + (1-\\omega) \\cdot \\text{ACC}_{train} \\big), $$\n", "\n", "we compute the weight $\\gamma$ as\n", "\n", @@ -73,7 +73,7 @@ "\n", "$$R = \\frac{(-1) \\times (\\text{ACC}_{h, i} - \\text{ACC}_{train})}{\\gamma - (1 -\\text{ACC}_{h, i})}.$$\n", "\n", - "(Since we are plugging $\\omega$ into the equation for computing $$ACC_{boot}$$ that we defined above, $$\\text{ACC}_{h, i}$$ and $\\text{ACC}_{r, i}$ still refer to the out-of-bag accuracy in the *i*th bootstrap round and the whole training set accuracy, respectively.)\n", + "(Since we are plugging $\\omega$ into the equation for computing $$ACC_{boot}$$ that we defined above, $$\\text{ACC}_{h, i}$$ and $\\text{ACC}_{train}$ still refer to the out-of-bag accuracy in the *i*th bootstrap round and the whole training set accuracy, respectively.)\n", "\n", "Further, we need to determine the *no-information rate* $\\gamma$ in order to compute *R*. For instance, we can compute $\\gamma$ by fitting a model to a dataset that contains all possible combinations between samples $x_{i'}$ and target class labels $y_{i}$ — we pretend that the observations and class labels are independent:\n", "\n", diff --git a/docs/sources/user_guide/feature_selection/SequentialFeatureSelector_files/SequentialFeatureSelector_44_1.png b/docs/sources/user_guide/feature_selection/SequentialFeatureSelector_files/SequentialFeatureSelector_44_1.png index 205054142..85a62c918 100644 Binary files a/docs/sources/user_guide/feature_selection/SequentialFeatureSelector_files/SequentialFeatureSelector_44_1.png and b/docs/sources/user_guide/feature_selection/SequentialFeatureSelector_files/SequentialFeatureSelector_44_1.png differ diff --git a/docs/sources/user_guide/feature_selection/SequentialFeatureSelector_files/SequentialFeatureSelector_47_0.png b/docs/sources/user_guide/feature_selection/SequentialFeatureSelector_files/SequentialFeatureSelector_47_0.png index 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