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Classifier comparison#

A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets.

Particularly in high-dimensional spaces, data can more easily be separated linearly and the simplicity of classifiers such as naive Bayes and linear SVMs might lead to better generalization than is achieved by other classifiers.

The plots show training points in solid colors and testing points semi-transparent. The lower right shows the classification accuracy on the test set.

Input data, Nearest Neighbors, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest, Neural Net, AdaBoost, Naive Bayes, QDA

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.pyplotasplt
importnumpyasnp
frommatplotlib.colorsimport ListedColormap
fromsklearn.datasetsimport make_circles , make_classification , make_moons
fromsklearn.discriminant_analysisimport QuadraticDiscriminantAnalysis
fromsklearn.ensembleimport AdaBoostClassifier , RandomForestClassifier
fromsklearn.gaussian_processimport GaussianProcessClassifier
fromsklearn.gaussian_process.kernelsimport RBF
fromsklearn.inspectionimport DecisionBoundaryDisplay
fromsklearn.model_selectionimport train_test_split
fromsklearn.naive_bayesimport GaussianNB
fromsklearn.neighborsimport KNeighborsClassifier
fromsklearn.neural_networkimport MLPClassifier
fromsklearn.pipelineimport make_pipeline
fromsklearn.preprocessingimport StandardScaler
fromsklearn.svmimport SVC
fromsklearn.treeimport DecisionTreeClassifier
names = [
 "Nearest Neighbors",
 "Linear SVM",
 "RBF SVM",
 "Gaussian Process",
 "Decision Tree",
 "Random Forest",
 "Neural Net",
 "AdaBoost",
 "Naive Bayes",
 "QDA",
]
classifiers = [
 KNeighborsClassifier (3),
 SVC (kernel="linear", C=0.025, random_state=42),
 SVC (gamma=2, C=1, random_state=42),
 GaussianProcessClassifier (1.0 * RBF (1.0), random_state=42),
 DecisionTreeClassifier (max_depth=5, random_state=42),
 RandomForestClassifier (
 max_depth=5, n_estimators=10, max_features=1, random_state=42
 ),
 MLPClassifier (alpha=1, max_iter=1000, random_state=42),
 AdaBoostClassifier (random_state=42),
 GaussianNB (),
 QuadraticDiscriminantAnalysis (),
]
X, y = make_classification (
 n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1
)
rng = np.random.RandomState (2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)
datasets = [
 make_moons (noise=0.3, random_state=0),
 make_circles (noise=0.2, factor=0.5, random_state=1),
 linearly_separable,
]
figure = plt.figure (figsize=(27, 9))
i = 1
# iterate over datasets
for ds_cnt, ds in enumerate(datasets):
 # preprocess dataset, split into training and test part
 X, y = ds
 X_train, X_test, y_train, y_test = train_test_split (
 X, y, test_size=0.4, random_state=42
 )
 x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
 y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
 # just plot the dataset first
 cm = plt.cm.RdBu
 cm_bright = ListedColormap (["#FF0000", "#0000FF"])
 ax = plt.subplot (len(datasets), len(classifiers) + 1, i)
 if ds_cnt == 0:
 ax.set_title("Input data")
 # Plot the training points
 ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
 # Plot the testing points
 ax.scatter(
 X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k"
 )
 ax.set_xlim(x_min, x_max)
 ax.set_ylim(y_min, y_max)
 ax.set_xticks(())
 ax.set_yticks(())
 i += 1
 # iterate over classifiers
 for name, clf in zip(names, classifiers):
 ax = plt.subplot (len(datasets), len(classifiers) + 1, i)
 clf = make_pipeline (StandardScaler (), clf)
 clf.fit(X_train, y_train)
 score = clf.score(X_test, y_test)
 DecisionBoundaryDisplay.from_estimator (
 clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5
 )
 # Plot the training points
 ax.scatter(
 X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k"
 )
 # Plot the testing points
 ax.scatter(
 X_test[:, 0],
 X_test[:, 1],
 c=y_test,
 cmap=cm_bright,
 edgecolors="k",
 alpha=0.6,
 )
 ax.set_xlim(x_min, x_max)
 ax.set_ylim(y_min, y_max)
 ax.set_xticks(())
 ax.set_yticks(())
 if ds_cnt == 0:
 ax.set_title(name)
 ax.text(
 x_max - 0.3,
 y_min + 0.3,
 ("%.2f" % score).lstrip("0"),
 size=15,
 horizontalalignment="right",
 )
 i += 1
plt.tight_layout ()
plt.show ()