快速入门Python机器学习（17）
8.5 LinearSVR
8.5.1 LinearSVR类参数、属性和方法
类
class sklearn.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)
参数
属性 | 类型 | 解释 |
|---|---|---|
C | float, default=1.0 | 正则化参数。正则化的强度与C成反比，必须是严格正的。 |
属性
属性 | 解释 |
|---|---|
coef_ | ndarray of shape (n_features) if n_classes == 2 else (n_classes, n_features)分配给特征的权重（原始问题中的系数）。这仅在线性内核的情况下可用。coef_是从遵循liblinear的内部内存布局的raw_coef_派生的只读属性。 |
intercept_ | ndarray of shape (1) if n_classes == 2 else (n_classes) 决策函数中的常数。 |
n_iter_ | int在所有类中运行的最大迭代次数。 |
方法
fit(X, y[, sample_weight]) | 根据给定的训练数据对模型进行拟合。 |
|---|---|
get_params([deep]) | 获取此估计器的参数。用 |
predict(X) | 线性模型预测。 |
score(X, y[, sample_weight]) | 返回预测的确定系数R2。 |
set_params(**params) | 设置此估计器的参数。 |
8.5.2 LinearSVR分析make_regression无噪音数据
def LinearSVR_for_make_regression():
myutil = util()
X,y = make_regression(n_samples=100,n_features=1,n_informative=2,random_state=8)
X_train,X_test,y_train,y_test = train_test_split(X, y, random_state=8,test_size=0.3)
clf = LinearSVR().fit(X,y)
title = "make_regression LinearSVR()回归线（无噪音）"
myutil.print_scores(clf,X_train,y_train,X_test,y_test,title)
myutil.draw_line(X[:,0],y,clf,title)
myutil.plot_learning_curve(LinearSVR(),X,y,title)
myutil.show_pic(title)输出
make_regression LineSVR()回归线（无噪音）:
100.00%
make_regression LineSVR()回归线（无噪音）:
100.00% 结果非常好
8.5.3LinearSVR分析make_regression有噪音数据
#加入噪音
def LinearSVR_for_make_regression_add_noise():
myutil = util()
X,y = make_regression(n_samples=100,n_features=1,n_informative=2,noise=50,random_state=8)
X_train,X_test,y_train,y_test = train_test_split(X, y, random_state=8,test_size=0.3)
clf = LinearSVR().fit(X,y)
title = "make_regression LinearSVR()回归线（有噪音）"
myutil.print_scores(clf,X_train,y_train,X_test,y_test,title)
myutil.draw_line(X[:,0],y,clf,title)
myutil.plot_learning_curve(LinearSVR(),X,y,title)
myutil.show_pic(title)输出
make_regression LinearSVR()回归线（有噪音）:
59.53%
make_regression LinearSVR()回归线（有噪音）:
51.15%结果欠拟合
8.5.4 LinearSVR分析波士顿房价数据
def LinearSVR_for_boston():
warnings.filterwarnings("ignore")
myutil = util()
boston = datasets.load_boston()
X,y = boston.data,boston.target
X_train,X_test,y_train,y_test = train_test_split(X, y, random_state =8)
scaler = StandardScaler()
scaler.fit(X_train)
X_scaler = scaler.transform(X)
X_train_scaler = scaler.transform(X_train)
X_test_scaler = scaler.transform(X_test)
boston = LinearSVR()
boston.fit(X_train_scaler,y_train)
title = "LinearSVR for Boston"
myutil.print_scores(boston,X_train_scaler,y_train,X_test_scaler,y_test,title)
myutil.plot_learning_curve(LinearSVR(),X_scaler,y,title)
myutil.show_pic(title)输出
LinearSVR for Boston:
70.37%
LinearSVR for Boston:
70.11%欠拟合
8.5.5 LinearSVR分析糖尿病数据
#分析糖尿病数据
def LinearSVR_for_diabetes():
warnings.filterwarnings("ignore")
myutil = util()
diabetes = datasets.load_diabetes()
X,y = diabetes.data,diabetes.target
X_train,X_test,y_train,y_test = train_test_split(X, y, random_state =8)
scaler = StandardScaler()
scaler.fit(X_train)
X_scaler = scaler.transform(X)
X_train_scaler = scaler.transform(X_train)
X_test_scaler = scaler.transform(X_test)
svr = LinearSVR()
svr.fit(X_train_scaler,y_train)
title = "LinearSVR for Diabetes"
myutil.print_scores(svr,X_train_scaler,y_train,X_test_scaler,y_test,title)
myutil.plot_learning_curve(LinearSVR(),X_scaler,y,title)
myutil.show_pic(title)输出
LinearSVR for Diabetes:
36.11%
LinearSVR for Diabetes:
33.77%8.6总结
数据 | 模型 | kernel | gamma | 训练集得分 | 测试集得分 |
|---|---|---|---|---|---|
鸢尾花数据 | SVC | linear | scale | 99.17% | 100.00% |
auto | 99.17% | 100.00% | |||
0.1 | 99.17% | 100.00% | |||
0.01 | 99.17% | 100.00% | |||
0.001 | 99.17% | 100.00% | |||
rbf | scale | 96.67% | 96.67% | ||
auto | 97.50% | 96.67% | |||
0.1 | 97.50% | 96.67% | |||
0.01 | 95.00% | 86.67% | |||
0.001 | 70.00% | 53.33% | |||
sigmoid | scale | 6.67% | 10.00% | ||
auto | 4.17% | 3.33% | |||
0.1 | 5.83% | 6.67% | |||
0.01 | 70.00% | 53.33% | |||
0.001 | 70.00% | 53.33% | |||
poly | scale | 98.33% | 93.33% | ||
auto | 99.17% | 93.33% | |||
0.1 | 97.50% | 93.33% | |||
0.01 | 89.17% | 86.67% | |||
0.001 | 57.50% | 50.00% | |||
LinearSVC | 95.83% | 96.67% | |||
红酒数据 | SVC | linear | scale | 100.00% | 91.67% |
auto | 100.00% | 91.67% | |||
0.1 | 100.00% | 91.67% | |||
0.01 | 100.00% | 91.67% | |||
0.001 | 100.00% | 91.67% | |||
rbf | scale | 71.13% | 66.67% | ||
auto | 100.00% | 50.00%（过拟合） | |||
0.1 | 100.00% | 50.00%（过拟合） | |||
0.01 | 99.30% | 69.44%（过拟合） | |||
0.001 | 83.80% | 72.22% | |||
sigmoid | scale | 18.31% | 22.22% | ||
auto | 39.44% | 41.67% | |||
0.1 | 39.44% | 41.67% | |||
0.01 | 39.44% | 41.67% | |||
0.001 | 39.44% | 41.67% | |||
poly | scale | 65.49% | 80.56% | ||
auto | 100.00% | 91.67% | |||
0.1 | 100.00% | 91.67% | |||
0.01 | 100.00% | 91.67% | |||
0.001 | 100.00% | 91.67% | |||
LinearSVC | 90.14% | 86.11% | |||
乳腺癌数据 | SVC | linear | scale | 94.51% | 91.23% |
auto | 94.51% | 91.23% | |||
0.1 | 94.51% | 91.23% | |||
0.01 | 94.51% | 91.23% | |||
0.001 | 94.51% | 91.23% | |||
rbf | scale | 91.87% | 91.23% | ||
auto | 100.00% | 56.14%（过拟合） | |||
0.1 | 100.00% | 56.14%（过拟合） | |||
0.01 | 100.00% | 55.26%（过拟合） | |||
0.001 | 98.02% | 89.47% | |||
sigmoid | scale | 48.79% | 38.60% | ||
auto | 64.40% | 56.14% | |||
0.1 | 64.40% | 56.14% | |||
0.01 | 64.40% | 56.14% | |||
0.001 | 64.40% | 56.14% | |||
poly | scale | 90.77% | 91.23% | ||
auto | 48.35% | 51.75% | |||
0.1 | 35.38% | 34.21% | |||
0.01 | 76.70% | 79.82% | |||
0.001 | 40.44% | 42.11% | |||
LinearSVR | 93.19% | 92.11% | |||
波士顿房价数据 | SVC | linear | scale | 70.56% | 69.84% |
auto | 70.56% | 69.84% | |||
0.1 | 70.56% | 69.84% | |||
0.01 | 70.56% | 69.84% | |||
0.001 | 70.56% | 69.84% | |||
rbf | scale | 66.50% | 69.46% | ||
auto | 66.50% | 69.46% | |||
0.1 | 64.18% | 66.96% | |||
0.01 | 56.81% | 60.37% | |||
0.001 | 22.75% | 24.48% | |||
sigmoid | scale | 56.44% | 63.41% | ||
auto | 56.44% | 63.41% | |||
0.1 | 35.92% | 39.85% | |||
0.01 | 49.05% | 52.26% | |||
0.001 | 13.84% | 14.82% | |||
poly | scale | 68.60% | 62.33% | ||
auto | 68.60% | 62.33% | |||
0.1 | 76.02% | 63.72% | |||
0.01 | 1.59% | 1.03% | |||
0.001 | -2.36% | -2.66% | |||
LinearSVC | 70.37% | 70.11% | |||
糖尿病 | SVR | linear | scale | -0.89% | 0.35% |
auto | -0.89% | 0.35% | |||
0.1 | -0.89% | 0.35% | |||
0.01 | -0.89% | 0.35% | |||
0.001 | -0.89% | 0.35% | |||
rbf | scale | 18.30% | 15.14% | ||
auto | -2.94% | -1.77% | |||
0.1 | -2.94% | -1.77% | |||
0.01 | -3.18% | -2.07% | |||
0.001 | -3.20% | -2.10% | |||
sigmoid | scale | 37.86% | 38.36% | ||
auto | -3.07% | -1.94% | |||
0.1 | -3.07% | -1.94% | |||
0.01 | -3.19% | -2.09% | |||
0.001 | -3.20% | -2.10% | |||
poly | scale | 23.87% | 31.86% | ||
auto | -3.20% | -2.10% | |||
0.1 | -3.20% | -2.10% | |||
0.01 | -3.20% | -2.10% | |||
0.001 | -3.20% | -2.10% | |||
LinearSVR | 36.11% | 33.77% | |||
结论：
- SVR不适合分析糖尿病数据
- SVC分析红酒和乳腺癌数据，某些情况出现过拟合现象
- SVC分析鸢尾花，红酒，乳腺癌相对比较好
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