bigdata-机器学习笔记(2)

机器学习是对原始数据数据(train_feature->train_label)运用算法训练生成模型，来预测新的数据(test_feature)， python的scklearn包含了很多算法。

步骤： 导入sklearn模块->选择分类器->创建分类器->拟合分类器->用拟合的分类器进行预测 ->评估模型的准确率->选择分类器，如此循环。

intro：

• 我是如何花了一年时间来学机器学习的
• machine-learning-in-a-week
• welcome to machine learning

naive bayes：

• naive bayes

  高斯朴素贝叶斯,朴素贝叶斯,归一化

• sklearn.naive_bayes.GaussianNB

  from sklearn.naive_bayes import GaussianNB clf = GaussianNB() clf.fit(X, Y) clf.predict(test_feature)

svm：

• svm video

• modules svm

  from sklearn import svm clf = svm.SVC() clf.fit(X, y) clf.predict(test_feature)

  kernel:The kernel function can be any of the following: linear: \langle x, x’\rangle. polynomial: (\gamma \langle x, x’\rangle + r)^d. d is specified by keyword degree, r by coef0. rbf: \exp(-\gamma |x-x’|^2). \gamma is specified by keyword gamma, must be greater than 0. sigmoid (\tanh(\gamma \langle x,x’\rangle + r)), where r is specified by coef0.

  You can define your own kernels by either giving the kernel as a python function or by precomputing the Gram matrix.

  避免过度拟合->调节参数 训练数据太大，svm会比较慢，数据有噪声，会出现过度拟合

decision trees:

根据训练数据找出决策边界
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
clf.predict(test_feature)

• modules tree

k nearest neighbors

>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from sklearn.neighbors import KNeighborsClassifier
>>> neigh = KNeighborsClassifier(n_neighbors=3)
>>> neigh.fit(X, y) 

• KNeighborsClassifier

adaboost

random forest