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machine-learning-practice's Introduction

机器学习算法实现

统计学习方法 李航

Features

  • 代码使用 scikit-learn 的形式组织，方便学习和实验
  • 代码按照统计学习方法中的思路实现，包含详细实现思路与注释

Example

# 加载数据
train_path = '../mnist/mnist_train.csv'
test_path = '../mnist/mnist_test.csv'
train_data, train_label = load_data(train_path)
test_data, test_label = load_data(test_path)

# 创建朴素贝叶斯分类器
nb = NaiveBayes(var_smoothing=1)
# 训练
nb.fit(train_data, train_label)
# 测试
nb.score(test_data, test_label)

数据集: mnist， 请使用 ./data/transMinist.py 生成

  • K 近邻： KNN

  • 感知机： Perceptron

  • 朴素贝叶斯：NaiveBayes

  • 决策树：DicisionTree

  • 逻辑斯谛回归：LogisticRegression

参考：

比赛实践与学习

  • 泰坦尼克生存预测

machine-learning-practice's People

Contributors

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