sklearn各模塊詳解（1）

四、.svm

from sklearn import svm

4.1 分類

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：指定核函數：linear,polynomial,rbf,sigmoid。還可以是自定義的核函數。
degree：當kernel指定爲polynomial時，指定多項式次數。
gamma：當kernel爲非linear時，指定$\gamma$的值。
coef0：當kernel爲polynomial或sigmoid時，指定r的值。
shrinking
probability
tol
cache_size
class_weight：在fit方法中設置，用於樣本不平衡問題。
verbose
max_iter
decision_function_shape：ovo表示一對一，ovr表示一對剩下的，
random_state：略

---

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)

---

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)

---

4.2 迴歸

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)

---

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)

---

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)

---

屬性：
support_vectors_
support_
n_support
.decision_function
dual_coef_：$y_i \alpha_i$或$\alpha_i - \alpha_i^*$
intercept_：略

---

---

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：近鄰數，即選擇最近的幾個樣本來分類。
radius
algorithm：算法：‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute`

---

2. .KDTree

---

3. .BallTree

---

方法：

---

---