support_vector_machine.py

# -*- coding: utf-8 -*-
"""Support Vector Machine.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1VW4uDVTF6pOlGHVg3Dxwgd4hsWeGkAUS

**Soft Margin Classification**
"""

import numpy as np
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
iris = datasets.load_iris()
X = iris["data"][:, (2, 3)] 
y = (iris["target"] == 2).astype(np.float64)
svm_clf = Pipeline([
    ("scaler", StandardScaler()),
    ("linear_svc", LinearSVC(C=1, loss="hinge")),
])
svm_clf.fit(X, y)

"""**Nonlinear SVM Classification**"""

from sklearn.datasets import make_moons
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
X, y = make_moons(n_samples=100, noise=0.15)
polynomial_svm_clf = Pipeline([
                            ("poly_features", PolynomialFeatures(degree=3)),
                            ("scaler", StandardScaler()),
                            ("svm_clf", LinearSVC(C=10, loss="hinge"))
                            ])
polynomial_svm_clf.fit(X, y)

"""**Polynomial Kernel**"""

from sklearn.svm import SVC
poly_kernel_svm_clf = Pipeline([
    ("scaler", StandardScaler()),
    ("svm_clf", SVC(kernel="poly", degree=3, coef0=1, C=5))
])
poly_kernel_svm_clf.fit(X, y)