SVM 代码示例

最新推荐文章于 2024-07-04 23:13:55 发布

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最新推荐文章于 2024-07-04 23:13:55 发布

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分类专栏：人工智能文章标签：机器学习 SVM

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本文通过使用Python的scikit-learn库实现支持向量机(SVM)分类器，并在地形数据集上进行训练与测试。从数据准备到模型训练、预测直至评估准确率，详细展示了SVM的工作流程。

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import sys

from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData

import matplotlib.pyplot as plt
import copy
import numpy as np
import pylab as pl

features_train, labels_train, features_test, labels_test = makeTerrainData()

########################## SVM #################################
### we handle the import statement and SVC creation for you here
from sklearn.svm import SVC

clf = SVC(kernel="linear")

#### now your job is to fit the classifier
#### using the training features/labels, and to
#### make a set of predictions on the test data

clf.fit(features_train, labels_train)

#### store your predictions in a list named pred

pred = clf.predict(features_test)

from sklearn.metrics import accuracy_score
acc = accuracy_score(pred, labels_test)

def submitAccuracy():
return acc