.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/ensemble/plot_forest_iris.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ensemble_plot_forest_iris.py: ==================================================================== 绘制鸢尾花数据集上树集成的决策边界 ==================================================================== 绘制在鸢尾花数据集的特征对上训练的随机树森林的决策边界。 此图比较了决策树分类器（第一列）、随机森林分类器（第二列）、极端随机树分类器（第三列）和AdaBoost分类器（第四列）学习到的决策边界。 在第一行中，分类器仅使用花萼宽度和花萼长度特征构建；在第二行中，仅使用花瓣长度和花萼长度特征构建；在第三行中，仅使用花瓣宽度和花瓣长度特征构建。 按质量降序排列，当在所有4个特征上使用30个估计器进行训练并使用10折交叉验证评分时，我们看到： ExtraTreesClassifier() # 0.95分 RandomForestClassifier() # 0.94分 AdaBoost(DecisionTree(max_depth=3)) # 0.94分 DecisionTree(max_depth=None) # 0.94分 增加AdaBoost的 `max_depth` 会降低分数的标准差（但平均分数不会提高）。 有关每个模型的更多详细信息，请参见控制台输出。 在此示例中，您可以尝试： 1) 改变 ``DecisionTreeClassifier`` 和 ``AdaBoostClassifier`` 的 ``max_depth`` ，例如尝试 ``DecisionTreeClassifier`` 的 ``max_depth=3`` 或 ``AdaBoostClassifier`` 的 ``max_depth=None`` 2) 改变 ``n_estimators`` 值得注意的是，随机森林和极端随机树可以在多核上并行拟合，因为每棵树是独立构建的。AdaBoost的样本是顺序构建的，因此不使用多核。 .. GENERATED FROM PYTHON SOURCE LINES 31-155 .. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_forest_iris_001.png :alt: Classifiers on feature subsets of the Iris dataset, DecisionTree, RandomForest, ExtraTrees, AdaBoost :srcset: /auto_examples/ensemble/images/sphx_glr_plot_forest_iris_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none DecisionTree with features [0, 1] has a score of 0.9266666666666666 RandomForest with 30 estimators with features [0, 1] has a score of 0.9266666666666666 ExtraTrees with 30 estimators with features [0, 1] has a score of 0.9266666666666666 AdaBoost with 30 estimators with features [0, 1] has a score of 0.82 DecisionTree with features [0, 2] has a score of 0.9933333333333333 RandomForest with 30 estimators with features [0, 2] has a score of 0.9933333333333333 ExtraTrees with 30 estimators with features [0, 2] has a score of 0.9933333333333333 AdaBoost with 30 estimators with features [0, 2] has a score of 0.9933333333333333 DecisionTree with features [2, 3] has a score of 0.9933333333333333 RandomForest with 30 estimators with features [2, 3] has a score of 0.9933333333333333 ExtraTrees with 30 estimators with features [2, 3] has a score of 0.9933333333333333 AdaBoost with 30 estimators with features [2, 3] has a score of 0.9866666666666667 | .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import ListedColormap from sklearn.datasets import load_iris from sklearn.ensemble import ( AdaBoostClassifier, ExtraTreesClassifier, RandomForestClassifier, ) from sklearn.tree import DecisionTreeClassifier # Parameters n_classes = 3 n_estimators = 30 cmap = plt.cm.RdYlBu plot_step = 0.02 # fine step width for decision surface contours plot_step_coarser = 0.5 # step widths for coarse classifier guesses RANDOM_SEED = 13 # fix the seed on each iteration # Load data iris = load_iris() plot_idx = 1 models = [ DecisionTreeClassifier(max_depth=None), RandomForestClassifier(n_estimators=n_estimators), ExtraTreesClassifier(n_estimators=n_estimators), AdaBoostClassifier( DecisionTreeClassifier(max_depth=3), n_estimators=n_estimators, algorithm="SAMME", ), ] for pair in ([0, 1], [0, 2], [2, 3]): for model in models: # 我们只取两个对应的特征 X = iris.data[:, pair] y = iris.target # Shuffle idx = np.arange(X.shape[0]) np.random.seed(RANDOM_SEED) np.random.shuffle(idx) X = X[idx] y = y[idx] # 标准化 mean = X.mean(axis=0) std = X.std(axis=0) X = (X - mean) / std # Train model.fit(X, y) scores = model.score(X, y) # 为每个列和控制台创建一个标题，使用 str() 并切掉字符串中无用的部分 model_title = str(type(model)).split(".")[-1][:-2][: -len("Classifier")] model_details = model_title if hasattr(model, "estimators_"): model_details += " with {} estimators".format(len(model.estimators_)) print(model_details + " with features", pair, "has a score of", scores) plt.subplot(3, 4, plot_idx) if plot_idx <= len(models): # 在每列的顶部添加一个标题 plt.title(model_title, fontsize=9) # 现在使用细网格作为输入绘制决策边界，并生成填充轮廓图 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid( np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step) ) # 绘制单个DecisionTreeClassifier或对分类器集成的决策边界进行alpha混合 if isinstance(model, DecisionTreeClassifier): Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) cs = plt.contourf(xx, yy, Z, cmap=cmap) else: # 选择 alpha 混合级别时要考虑正在使用的估计器数量（需要注意的是，如果 AdaBoost 提前达到了足够好的拟合效果，它可以使用比最大数量更少的估计器）。 estimator_alpha = 1.0 / len(model.estimators_) for tree in model.estimators_: Z = tree.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) cs = plt.contourf(xx, yy, Z, alpha=estimator_alpha, cmap=cmap) # 构建一个更粗略的网格来绘制一组集成分类，以展示这些分类与我们在决策边界中看到的有何不同。这些点是规则分布的，并且没有黑色轮廓。 xx_coarser, yy_coarser = np.meshgrid( np.arange(x_min, x_max, plot_step_coarser), np.arange(y_min, y_max, plot_step_coarser), ) Z_points_coarser = model.predict( np.c_[xx_coarser.ravel(), yy_coarser.ravel()] ).reshape(xx_coarser.shape) cs_points = plt.scatter( xx_coarser, yy_coarser, s=15, c=Z_points_coarser, cmap=cmap, edgecolors="none", ) # 绘制训练点，这些点聚集在一起并有黑色轮廓。 plt.scatter( X[:, 0], X[:, 1], c=y, cmap=ListedColormap(["r", "y", "b"]), edgecolor="k", s=20, ) plot_idx += 1 # move on to the next plot in sequence plt.suptitle("Classifiers on feature subsets of the Iris dataset", fontsize=12) plt.axis("tight") plt.tight_layout(h_pad=0.2, w_pad=0.2, pad=2.5) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.591 seconds) .. _sphx_glr_download_auto_examples_ensemble_plot_forest_iris.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_forest_iris.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_forest_iris.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_forest_iris.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_forest_iris.zip ` .. include:: plot_forest_iris.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_