跟着Leo机器学习：sklearn之Feature selection

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sklearn框架

函数导图

1.13. Feature selection

1.13.1. Removing features with low variance

from sklearn.feature_selection import VarianceThreshold
X = [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]]
sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
sel.fit_transform(X)

1.13.2. Univariate feature selection

from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
X, y = load_iris(return_X_y=True)
X.shape
X_new = SelectKBest(chi2, k=2).fit_transform(X, y)

X_new.shape

1.13.4. Feature selection using SelectFromModel

1.13.4.1. L1-based feature selection

from sklearn.svm import LinearSVC
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectFromModel
X, y = load_iris(return_X_y=True)
X.shape
lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(X, y)

model = SelectFromModel(lsvc, prefit=True)

X_new = model.transform(X)

X_new.shape

1.13.4.2. Tree-based feature selection

from sklearn.ensemble import ExtraTreesClassifier
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectFromModel
X, y = load_iris(return_X_y=True)
X.shape
clf = ExtraTreesClassifier(n_estimators=50)

clf = clf.fit(X, y)

clf.feature_importances_
model = SelectFromModel(clf, prefit=True)

X_new = model.transform(X)

X_new.shape

1.13.5. Feature selection as part of a pipeline

clf = Pipeline([
  ('feature_selection', SelectFromModel(LinearSVC(penalty="l1"))),
  ('classification', RandomForestClassifier())
])
clf.fit(X, y)

源地址

https://scikit-learn.org/stable/modules/feature_selection.html