sklearn各模块详解（1）

四、.svm

from sklearn import svm

4.1 分类

1. svm.SVC(C=1.0, kernel=’rbf’, degree=3, gamma=’auto_deprecated’, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=’ovr’, random_state=None)

C
kernel：指定核函数：linear,polynomial,rbf,sigmoid。还可以是自定义的核函数。
degree：当kernel指定为polynomial时，指定多项式次数。
gamma：当kernel为非linear时，指定$\gamma$的值。
coef0：当kernel为polynomial或sigmoid时，指定r的值。
shrinking
probability
tol
cache_size
class_weight：在fit方法中设置，用于样本不平衡问题。
verbose
max_iter
decision_function_shape：ovo表示一对一，ovr表示一对剩下的，
random_state：略

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2. svm.NuSVC(nu=0.5, kernel=’rbf’, degree=3, gamma=’auto_deprecated’, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=’ovr’, random_state=None)

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3. svm.LinearSVC(penalty=’l2’, loss=’squared_hinge’, dual=True, tol=0.0001, C=1.0, multi_class=’ovr’, fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)

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4.2 回归

1. svm.SVR(kernel=’rbf’, degree=3, gamma=’auto_deprecated’, coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)

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2. svm.NuSVR(nu=0.5, C=1.0, kernel=’rbf’, degree=3, gamma=’auto_deprecated’, coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1)

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3. svm.LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss=’epsilon_insensitive’, fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000)

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属性：
support_vectors_
support_
n_support
.decision_function
dual_coef_：$y_i{\alpha }_i$或${\alpha }_i-{\alpha }_i^{\ast }$
intercept_：略

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五、.neighbors

1. .NearestNeighbors(n_neighbors=5, radius=1.0, algorithm=’auto’, leaf_size=30, metric=’minkowski’, p=2, metric_params=None, n_jobs=None, **kwargs)

n_neighbors：近邻数，即选择最近的几个样本来分类。
radius
algorithm：算法：‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute`

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2. .KDTree

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3. .BallTree

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方法：

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