지도학습 - LinearSVM_2

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.datasets import load_iris

iris = load_iris()

In [2]:

from sklearn.model_selection import train_test_split

col1 = 0
col2 = 1

X = iris.data[:,[col1,col2]] # 시각화를 위해 속성 2개만 선정 (petal length & petal width)
# y = iris.target
y = iris.target.copy()
y[y==0] = 2
y[y==1] = 0
y[y==2] = 1
#y[y==2]=1, np.where(y==2,1,y), np.where(y==0,0,1)
#setosa=0 나머지는 1로 해서 setosa와 나머지로 분류

X_train, X_test, y_train, y_test = train_test_split(X, y)
X_train.shape, X_test.shape, y_train.shape, y_test.shape

Out[2]:

((112, 2), (38, 2), (112,), (38,))

In [3]:

plt.figure(figsize=[8,6])
plt.scatter(X[:,0], X[:,1], c=y)
plt.colorbar()

Out[3]:

<matplotlib.colorbar.Colorbar at 0x21251b5c5c0>

In [4]:

from sklearn.svm import LinearSVC

model = LinearSVC(C=1)
model.fit(X_train, y_train)

score = model.score(X_test, y_test)
print(score)

0.6578947368421053

In [5]:

# y = np.where(iris.target==2, 0, 1)

In [6]:

import mglearn

plt.figure(figsize=[8,6])
mglearn.plots.plot_2d_classification(model, X_train, eps=0.5, cm='spring')
mglearn.discrete_scatter(X_train[:,0], X_train[:,1], y_train)

Out[6]:

[<matplotlib.lines.Line2D at 0x21253484f28>,
 <matplotlib.lines.Line2D at 0x21253495080>]

In [7]:

import mglearn

plt.figure(figsize=[8,6])
mglearn.plots.plot_2d_classification(model, X_train, eps=0.5, cm='spring')
mglearn.discrete_scatter(X_test[:,0], X_test[:,1], y_test)

Out[7]:

[<matplotlib.lines.Line2D at 0x2125344b7f0>,
 <matplotlib.lines.Line2D at 0x212534cbf60>]

In [14]:

from sklearn.svm import LinearSVC

model = LinearSVC(C=0.01)
model.fit(X_train, y_train)

score = model.score(X_test, y_test)
print(score)

0.6052631578947368

In [13]:

import mglearn
import matplotlib.pyplot as plt

plt.figure(figsize=[5,4])
mglearn.plots.plot_2d_classification(model, X_train, eps=0.5, cm='spring') # cm을 이용해 색 조절
mglearn.discrete_scatter(X_train[:,0], X_train[:,1], y_train)

Out[13]:

[<matplotlib.lines.Line2D at 0x21253962eb8>,
 <matplotlib.lines.Line2D at 0x21253962fd0>]

In [21]:

from sklearn.svm import LinearSVC

model = LinearSVC(C=250)
model.fit(X_train, y_train)

score = model.score(X_test, y_test)
print(score)

0.6842105263157895

In [22]:

import mglearn
import matplotlib.pyplot as plt

plt.figure(figsize=[5,4])
mglearn.plots.plot_2d_classification(model, X_train, eps=0.5, cm='spring') # cm을 이용해 색 조절
mglearn.discrete_scatter(X_train[:,0], X_train[:,1], y_train)

Out[22]:

[<matplotlib.lines.Line2D at 0x21253a4aef0>,
 <matplotlib.lines.Line2D at 0x21253a53048>]