🔠分類範例¶

SVM使用範例¶

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%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline import numpy as np import matplotlib.pyplot as plt

載入iris資料集¶

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from sklearn.datasets import load_iris

from sklearn.datasets import load_iris

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iris = load_iris()

iris = load_iris()

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iris.feature_names

iris.feature_names

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iris.target_names

iris.target_names

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X = iris.data
Y = iris.target

X = iris.data Y = iris.target

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len(X)

len(X)

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X[0]

X[0]

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Y

Y

產生訓練集跟測試集¶

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from sklearn.model_selection import train_test_split

from sklearn.model_selection import train_test_split

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x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)

使用SVM來做分類¶

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#1. 載入模型
from sklearn.svm import SVC

#1. 載入模型 from sklearn.svm import SVC

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#2. 建立模型
clf = SVC()

#2. 建立模型 clf = SVC()

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#3. 訓練模型
clf.fit(x_train, y_train)

#3. 訓練模型 clf.fit(x_train, y_train)

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#4. 使用模型來做預測
y_predict = clf.predict(x_test)

#4. 使用模型來做預測 y_predict = clf.predict(x_test)

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y_predict

y_predict

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# 計算模型的Accuracy
clf.score(x_test, y_test)

# 計算模型的Accuracy clf.score(x_test, y_test)