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| .. _multiclass_examples: | ||
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| Multiclass methods | ||
| ------------------ | ||
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| Examples concerning the :mod:`sklearn.multiclass` module. |
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| """ | ||
| =============================================== | ||
| Overview of multiclass training meta-estimators | ||
| =============================================== | ||
| In this example, we discuss the problem of classification when the target | ||
| variable is composed of more than two classes. This is called multiclass | ||
| classification. | ||
| In scikit-learn, all estimators support multiclass classification out of the | ||
| box: the most sensible strategy was implemented for the end-user. The | ||
| :mod:`sklearn.multiclass` module implements various strategies that one can use | ||
| for experimenting or developing third-party estimators that only support binary | ||
| classification. | ||
| :mod:`sklearn.multiclass` includes OvO/OvR strategies used to train a | ||
| multiclass classifier by fitting a set of binary classifiers (the | ||
| :class:`~sklearn.multiclass.OneVsOneClassifier` and | ||
| :class:`~sklearn.multiclass.OneVsRestClassifier` meta-estimators). This example | ||
| will review them. | ||
| """ | ||
|
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| # %% | ||
| # The Yeast UCI dataset | ||
| # --------------------- | ||
| # | ||
| # In this example, we use a UCI dataset [1]_, generally referred as the Yeast | ||
| # dataset. We use the :func:`sklearn.datasets.fetch_openml` function to load | ||
| # the dataset from OpenML. | ||
| from sklearn.datasets import fetch_openml | ||
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| X, y = fetch_openml(data_id=181, as_frame=True, return_X_y=True, parser="pandas") | ||
|
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| # %% | ||
| # To know the type of data science problem we are dealing with, we can check | ||
| # the target for which we want to build a predictive model. | ||
| y.value_counts().sort_index() | ||
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| # %% | ||
| # We see that the target is discrete and composed of 10 classes. We therefore | ||
| # deal with a multiclass classification problem. | ||
| # | ||
| # Strategies comparison | ||
| # --------------------- | ||
| # | ||
| # In the following experiment, we use a | ||
| # :class:`~sklearn.tree.DecisionTreeClassifier` and a | ||
| # :class:`~sklearn.model_selection.RepeatedStratifiedKFold` cross-validation | ||
| # with 3 splits and 5 repetitions. | ||
| # | ||
| # We compare the following strategies: | ||
| # | ||
| # * :class:~sklearn.tree.DecisionTreeClassifier can handle multiclass | ||
| # classification without needing any special adjustments. It works by breaking | ||
| # down the training data into smaller subsets and focusing on the most common | ||
| # class in each subset. By repeating this process, the model can accurately | ||
| # classify input data into multiple different classes. | ||
| # * :class:`~sklearn.multiclass.OneVsOneClassifier` trains a set of binary | ||
| # classifiers where each classifier is trained to distinguish between | ||
| # two classes. | ||
| # * :class:`~sklearn.multiclass.OneVsRestClassifier`: trains a set of binary | ||
| # classifiers where each classifier is trained to distinguish between | ||
| # one class and the rest of the classes. | ||
| # * :class:`~sklearn.multiclass.OutputCodeClassifier`: trains a set of binary | ||
| # classifiers where each classifier is trained to distinguish between | ||
| # a set of classes from the rest of the classes. The set of classes is | ||
| # defined by a codebook, which is randomly generated in scikit-learn. This | ||
| # method exposes a parameter `code_size` to control the size of the codebook. | ||
| # We set it above one since we are not interested in compressing the class | ||
| # representation. | ||
| import pandas as pd | ||
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| from sklearn.model_selection import RepeatedStratifiedKFold, cross_validate | ||
| from sklearn.multiclass import ( | ||
| OneVsOneClassifier, | ||
| OneVsRestClassifier, | ||
| OutputCodeClassifier, | ||
| ) | ||
| from sklearn.tree import DecisionTreeClassifier | ||
|
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| cv = RepeatedStratifiedKFold(n_splits=3, n_repeats=5, random_state=0) | ||
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| tree = DecisionTreeClassifier(random_state=0) | ||
| ovo_tree = OneVsOneClassifier(tree) | ||
| ovr_tree = OneVsRestClassifier(tree) | ||
| ecoc = OutputCodeClassifier(tree, code_size=2) | ||
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| cv_results_tree = cross_validate(tree, X, y, cv=cv, n_jobs=2) | ||
| cv_results_ovo = cross_validate(ovo_tree, X, y, cv=cv, n_jobs=2) | ||
| cv_results_ovr = cross_validate(ovr_tree, X, y, cv=cv, n_jobs=2) | ||
| cv_results_ecoc = cross_validate(ecoc, X, y, cv=cv, n_jobs=2) | ||
|
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| # %% | ||
| # We can now compare the statistical performance of the different strategies. | ||
| # We plot the score distribution of the different strategies. | ||
| from matplotlib import pyplot as plt | ||
|
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||
| scores = pd.DataFrame( | ||
| { | ||
| "DecisionTreeClassifier": cv_results_tree["test_score"], | ||
| "OneVsOneClassifier": cv_results_ovo["test_score"], | ||
| "OneVsRestClassifier": cv_results_ovr["test_score"], | ||
| "OutputCodeClassifier": cv_results_ecoc["test_score"], | ||
| } | ||
| ) | ||
| ax = scores.plot.kde(legend=True) | ||
| ax.set_xlabel("Accuracy score") | ||
| ax.set_xlim([0, 0.7]) | ||
| _ = ax.set_title( | ||
| "Density of the accuracy scores for the different multiclass strategies" | ||
| ) | ||
|
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||
| # %% | ||
| # At a first glance, we can see that the built-in strategy of the decision | ||
| # tree classifier is working quite well. One-vs-one and the error-correcting | ||
| # output code strategies are working even better. However, the | ||
| # one-vs-rest strategy is not working as well as the other strategies. | ||
| # | ||
| # Indeed, these results reproduce something reported in the literature | ||
| # as in [2]_. However, the story is not as simple as it seems. | ||
| # | ||
| # The importance of hyperparameters search | ||
| # ---------------------------------------- | ||
| # | ||
| # It was later shown in [3]_ that the multiclass strategies would show similar | ||
| # scores if the hyperparameters of the base classifiers are first optimized. | ||
| # | ||
| # Here we try to reproduce such result by at least optimizing the depth of the | ||
| # base decision tree. | ||
| from sklearn.model_selection import GridSearchCV | ||
|
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||
| param_grid = {"max_depth": [3, 5, 8]} | ||
| tree_optimized = GridSearchCV(tree, param_grid=param_grid, cv=3) | ||
| ovo_tree = OneVsOneClassifier(tree_optimized) | ||
| ovr_tree = OneVsRestClassifier(tree_optimized) | ||
| ecoc = OutputCodeClassifier(tree_optimized, code_size=2) | ||
|
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| cv_results_tree = cross_validate(tree_optimized, X, y, cv=cv, n_jobs=2) | ||
| cv_results_ovo = cross_validate(ovo_tree, X, y, cv=cv, n_jobs=2) | ||
| cv_results_ovr = cross_validate(ovr_tree, X, y, cv=cv, n_jobs=2) | ||
| cv_results_ecoc = cross_validate(ecoc, X, y, cv=cv, n_jobs=2) | ||
|
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| scores = pd.DataFrame( | ||
| { | ||
| "DecisionTreeClassifier": cv_results_tree["test_score"], | ||
| "OneVsOneClassifier": cv_results_ovo["test_score"], | ||
| "OneVsRestClassifier": cv_results_ovr["test_score"], | ||
| "OutputCodeClassifier": cv_results_ecoc["test_score"], | ||
| } | ||
| ) | ||
| ax = scores.plot.kde(legend=True) | ||
| ax.set_xlabel("Accuracy score") | ||
| ax.set_xlim([0, 0.7]) | ||
| _ = ax.set_title( | ||
| "Density of the accuracy scores for the different multiclass strategies" | ||
| ) | ||
|
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| plt.show() | ||
|
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||
| # %% | ||
| # We can see that once the hyperparameters are optimized, all multiclass | ||
| # strategies have similar performance as discussed in [3]_. | ||
| # | ||
| # Conclusion | ||
| # ---------- | ||
| # | ||
| # We can get some intuition behind those results. | ||
| # | ||
| # First, the reason for which one-vs-one and error-correcting output code are | ||
| # outperforming the tree when the hyperparameters are not optimized relies on | ||
| # fact that they ensemble a larger number of classifiers. The ensembling | ||
| # improves the generalization performance. This is a bit similar why a bagging | ||
| # classifier generally performs better than a single decision tree if no care | ||
| # is taken to optimize the hyperparameters. | ||
| # | ||
| # Then, we see the importance of optimizing the hyperparameters. Indeed, it | ||
| # should be regularly explored when developing predictive models even if | ||
| # techniques such as ensembling help at reducing this impact. | ||
| # | ||
| # Finally, it is important to recall that the estimators in scikit-learn | ||
| # are developed with a specific strategy to handle multiclass classification | ||
| # out of the box. So for these estimators, it means that there is no need to | ||
| # use different strategies. These strategies are mainly useful for third-party | ||
| # estimators supporting only binary classification. In all cases, we also show | ||
| # that the hyperparameters should be optimized. | ||
| # | ||
| # References | ||
| # ---------- | ||
| # | ||
| # .. [1] https://archive.ics.uci.edu/ml/datasets/Yeast | ||
| # | ||
| # .. [2] `"Reducing multiclass to binary: A unifying approach for margin classifiers." | ||
| # Allwein, Erin L., Robert E. Schapire, and Yoram Singer. | ||
| # Journal of machine learning research 1 | ||
| # Dec (2000): 113-141. | ||
| # <https://www.jmlr.org/papers/volume1/allwein00a/allwein00a.pdf>`_. | ||
| # | ||
| # .. [3] `"In defense of one-vs-all classification." | ||
| # Journal of Machine Learning Research 5 | ||
| # Jan (2004): 101-141. | ||
| # <https://www.jmlr.org/papers/volume5/rifkin04a/rifkin04a.pdf>`_. |
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