From bd9cb31f6476c774014765adf0b306d37275615a Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Wed, 1 Mar 2017 21:55:04 +0800 Subject: [PATCH 1/9] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=86=B3=E7=AD=96?= =?UTF-8?q?=E6=A0=91=E6=B5=81=E7=A8=8B=E4=BB=8B=E7=BB=8D=E5=9B=BE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/3.决策树.md | 2 ++ docs/3.决策树流程介绍图.jpg | Bin 0 -> 57137 bytes 2 files changed, 2 insertions(+) create mode 100644 docs/3.决策树流程介绍图.jpg diff --git a/docs/3.决策树.md b/docs/3.决策树.md index 4a2db238..65494b33 100644 --- a/docs/3.决策树.md +++ b/docs/3.决策树.md @@ -23,3 +23,5 @@ * \\(H = -\sum_{i=0}^np(x_i)\log_2p(x_i)\\) 表示香农熵，用于计算信息熵 * 基尼不纯度(Gini impurity) [本书不做过多的介绍] * 简单来说：就是从一个数据集中随机选取子项，度量其被错误分类到其他分组里的概率。 +* 流程介绍图 +* ![决策树流程介绍图](./3.决策树流程介绍图.jpg) \ No newline at end of file diff --git a/docs/3.决策树流程介绍图.jpg b/docs/3.决策树流程介绍图.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d5908deda54d6228e6f1582bff74118365fc8ee4 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docs/9.树回归.md | 13 +++ src/python/09.RegTrees/regTrees.py | 140 +++++++++++++++++++++++++++++ 3 files changed, 156 insertions(+), 1 deletion(-) create mode 100644 docs/9.树回归.md create mode 100644 src/python/09.RegTrees/regTrees.py diff --git a/README.md b/README.md index fc123341..0d56cfa5 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # MachineLearning -**Mahchine Leaning in Action (python)** +**Mahchine Leaning in Action (python) | ApacheCN(apache中文网)** ## 第一部分 分类 @@ -19,6 +19,7 @@ * 8) 预测数值型数据：回归 * 9) 数回归 + * [树回归](./docs/9.树回归.md) ## 第三部分 无监督学习 @@ -41,3 +42,4 @@ * 附录D 资源 * 索引 * 版权声明 +* [ApacheCN(apache中文网) 更新](wwww.apache.wiki) \ No newline at end of file diff --git a/docs/9.树回归.md b/docs/9.树回归.md new file mode 100644 index 00000000..bd68557c --- /dev/null +++ b/docs/9.树回归.md @@ -0,0 +1,13 @@ + +# 9) 树回归 + +* 树回归是什么？ + * 分类回归树(Classification and Regression Tree，CART)是一种典型的决策树算法，CART算法不仅可以应用于分类问题，而且可以用于回归问题。 + * CART算法构建的回归树并介绍其中的树剪枝技术(该技术主要的目的是防止数的过拟合) +* 树回归的构建 + * 优点：可以对复杂和非线性的数据建模。 + * 缺点：结果不易理解。 + * 适用数据类型：数值型和标称型数据。 + + + \ No newline at end of file diff --git a/src/python/09.RegTrees/regTrees.py b/src/python/09.RegTrees/regTrees.py new file mode 100644 index 00000000..c1436e0e --- /dev/null +++ b/src/python/09.RegTrees/regTrees.py @@ -0,0 +1,140 @@ +#!/usr/bin/python +# coding:utf8 + +''' +Created on Feb 4, 2011 +Update on 2017-03-02 +Tree-Based Regression Methods Source Code for Machine Learning in Action Ch. 9 +@author: Peter Harrington/jiangzhonglian +''' +from numpy import * + +def loadDataSet(fileName): #general function to parse tab -delimited floats + dataMat = [] #assume last column is target value + fr = open(fileName) + for line in fr.readlines(): + curLine = line.strip().split('\t') + fltLine = map(float,curLine) #map all elements to float() + dataMat.append(fltLine) + return dataMat + +def binSplitDataSet(dataSet, feature, value): + mat0 = dataSet[nonzero(dataSet[:,feature] > value)[0],:][0] + mat1 = dataSet[nonzero(dataSet[:,feature] <= value)[0],:][0] + return mat0,mat1 + +def regLeaf(dataSet):#returns the value used for each leaf + return mean(dataSet[:,-1]) + +def regErr(dataSet): + return var(dataSet[:,-1]) * shape(dataSet)[0] + +def linearSolve(dataSet): #helper function used in two places + m,n = shape(dataSet) + X = mat(ones((m,n))); Y = mat(ones((m,1)))#create a copy of data with 1 in 0th postion + X[:,1:n] = dataSet[:,0:n-1]; Y = dataSet[:,-1]#and strip out Y + xTx = X.T*X + if linalg.det(xTx) == 0.0: + raise NameError('This matrix is singular, cannot do inverse,\n\ + try increasing the second value of ops') + ws = xTx.I * (X.T * Y) + return ws,X,Y + +def modelLeaf(dataSet):#create linear model and return coeficients + ws,X,Y = linearSolve(dataSet) + return ws + +def modelErr(dataSet): + ws,X,Y = linearSolve(dataSet) + yHat = X * ws + return sum(power(Y - yHat,2)) + +def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)): + tolS = ops[0]; tolN = ops[1] + #if all the target variables are the same value: quit and return value + if len(set(dataSet[:,-1].T.tolist()[0])) == 1: #exit cond 1 + return None, leafType(dataSet) + m,n = shape(dataSet) + #the choice of the best feature is driven by Reduction in RSS error from mean + S = errType(dataSet) + bestS = inf; bestIndex = 0; bestValue = 0 + for featIndex in range(n-1): + for splitVal in set(dataSet[:,featIndex]): + mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal) + if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): continue + newS = errType(mat0) + errType(mat1) + if newS < bestS: + bestIndex = featIndex + bestValue = splitVal + bestS = newS + #if the decrease (S-bestS) is less than a threshold don't do the split + if (S - bestS) < tolS: + return None, leafType(dataSet) #exit cond 2 + mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue) + if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): #exit cond 3 + return None, leafType(dataSet) + return bestIndex,bestValue#returns the best feature to split on + #and the value used for that split + +def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):#assume dataSet is NumPy Mat so we can array filtering + feat, val = chooseBestSplit(dataSet, leafType, errType, ops)#choose the best split + if feat == None: return val #if the splitting hit a stop condition return val + retTree = {} + retTree['spInd'] = feat + retTree['spVal'] = val + lSet, rSet = binSplitDataSet(dataSet, feat, val) + retTree['left'] = createTree(lSet, leafType, errType, ops) + retTree['right'] = createTree(rSet, leafType, errType, ops) + return retTree + +def isTree(obj): + return (type(obj).__name__=='dict') + +def getMean(tree): + if isTree(tree['right']): tree['right'] = getMean(tree['right']) + if isTree(tree['left']): tree['left'] = getMean(tree['left']) + return (tree['left']+tree['right'])/2.0 + +def prune(tree, testData): + if shape(testData)[0] == 0: return getMean(tree) #if we have no test data collapse the tree + if (isTree(tree['right']) or isTree(tree['left'])):#if the branches are not trees try to prune them + lSet, rSet = binSplitDataSet(testData, tree['spInd'], tree['spVal']) + if isTree(tree['left']): tree['left'] = prune(tree['left'], lSet) + if isTree(tree['right']): tree['right'] = prune(tree['right'], rSet) + #if they are now both leafs, see if we can merge them + if not isTree(tree['left']) and not isTree(tree['right']): + lSet, rSet = binSplitDataSet(testData, tree['spInd'], tree['spVal']) + errorNoMerge = sum(power(lSet[:,-1] - tree['left'],2)) +\ + sum(power(rSet[:,-1] - tree['right'],2)) + treeMean = (tree['left']+tree['right'])/2.0 + errorMerge = sum(power(testData[:,-1] - treeMean,2)) + if errorMerge < errorNoMerge: + print "merging" + return treeMean + else: return tree + else: return tree + +def regTreeEval(model, inDat): + return float(model) + +def modelTreeEval(model, inDat): + n = shape(inDat)[1] + X = mat(ones((1,n+1))) + X[:,1:n+1]=inDat + return float(X*model) + +def treeForeCast(tree, inData, modelEval=regTreeEval): + if not isTree(tree): return modelEval(tree, inData) + if inData[tree['spInd']] > tree['spVal']: + if isTree(tree['left']): return treeForeCast(tree['left'], inData, modelEval) + else: return modelEval(tree['left'], inData) + else: + if isTree(tree['right']): return treeForeCast(tree['right'], inData, modelEval) + else: return modelEval(tree['right'], inData) + +def createForeCast(tree, testData, modelEval=regTreeEval): + m=len(testData) + yHat = mat(zeros((m,1))) + for i in range(m): + yHat[i,0] = treeForeCast(tree, mat(testData[i]), modelEval) + return yHat \ No newline at end of file From 38c6cbc292cce9fea778a151de026856969151f5 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 3 Mar 2017 15:20:28 +0800 Subject: [PATCH 3/9] =?UTF-8?q?=E6=9B=B4=E6=96=B0Readme=20=E6=B7=BB?= =?UTF-8?q?=E5=8A=A0ApachCN=E4=BF=A1=E6=81=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 0d56cfa5..0ae96161 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # MachineLearning -**Mahchine Leaning in Action (python) | ApacheCN(apache中文网)** +**Mahchine Leaning in Action (python) | [ApacheCN(apache中文网)](http://wwww.apache.wiki)** ## 第一部分 分类 @@ -42,4 +42,4 @@ * 附录D 资源 * 索引 * 版权声明 -* [ApacheCN(apache中文网) 更新](wwww.apache.wiki) \ No newline at end of file +* [ApacheCN(apache中文网) 维护更新](http://wwww.apache.wiki) \ No newline at end of file From 53e54a6ac9b653443c436fd8058073af8ff5ca94 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 3 Mar 2017 15:21:05 +0800 Subject: [PATCH 4/9] =?UTF-8?q?=E6=9B=B4=E6=96=B0Readme?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 0ae96161..281a773d 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # MachineLearning -**Mahchine Leaning in Action (python) | [ApacheCN(apache中文网)](http://wwww.apache.wiki)** +**Mahchine Leaning in Action (python) | [ApacheCN(apache中文网)](http://www.apache.wiki)** ## 第一部分 分类 @@ -42,4 +42,4 @@ * 附录D 资源 * 索引 * 版权声明 -* [ApacheCN(apache中文网) 维护更新](http://wwww.apache.wiki) \ No newline at end of file +* [ApacheCN(apache中文网) 维护更新](http://www.apache.wiki) \ No newline at end of file From 37bc27e0e40de3d610c51577353f5e7624e67d5c Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Fri, 3 Mar 2017 15:23:17 +0800 Subject: [PATCH 5/9] =?UTF-8?q?=E6=9B=B4=E6=96=B0ML=E9=93=BE=E6=8E=A5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 281a773d..d95720a4 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # MachineLearning -**Mahchine Leaning in Action (python) | [ApacheCN(apache中文网)](http://www.apache.wiki)** +**Mahchine Leaning in Action (python) | [ApacheCN(apache中文网)](http://www.apache.wiki/display/ML)** ## 第一部分 分类 @@ -42,4 +42,4 @@ * 附录D 资源 * 索引 * 版权声明 -* [ApacheCN(apache中文网) 维护更新](http://www.apache.wiki) \ No newline at end of file +* [ApacheCN(apache中文网) 维护更新](http://www.apache.wiki/display/ML) \ No newline at end of file From 10e1c2b5772b233f474c619bf490886c83a45003 Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Mon, 6 Mar 2017 21:13:32 +0800 Subject: [PATCH 6/9] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=9E=84=E5=BB=BA?= =?UTF-8?q?=E6=A0=91=E7=9A=84Coding?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 2 +- docs/9.树回归.md | 6 +- src/python/01.NumPy.py | 9 +- src/python/03.DecisionTree/DTSklearn.py | 1 + .../03.DecisionTree/DecisionTreePlot.py | 6 +- src/python/09.RegTrees/TreeNode.py | 16 ++ src/python/09.RegTrees/regTrees.py | 184 ++++++++++++---- testData/RT_data1.txt | 200 ++++++++++++++++++ testData/RT_data2.txt | 200 ++++++++++++++++++ 9 files changed, 569 insertions(+), 55 deletions(-) create mode 100644 src/python/09.RegTrees/TreeNode.py create mode 100755 testData/RT_data1.txt create mode 100755 testData/RT_data2.txt diff --git a/README.md b/README.md index 31165d7f..92bdc294 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ ## 第四部分 其他工具 * 13) 使用PCA来简化数据 - *[利用PCA来简化数据](./docs/13.利用PCA来简化数据.md) + * [利用PCA来简化数据](./docs/13.利用PCA来简化数据.md) * 14) 使用SVD简化数据 * 15) 大数据与MapReduce diff --git a/docs/9.树回归.md b/docs/9.树回归.md index bd68557c..f695bff5 100644 --- a/docs/9.树回归.md +++ b/docs/9.树回归.md @@ -8,6 +8,6 @@ * 优点：可以对复杂和非线性的数据建模。 * 缺点：结果不易理解。 * 适用数据类型：数值型和标称型数据。 - - - \ No newline at end of file +* 那么问题来了，如何计算连续型数值的混乱度呢？ + * `误差`：也就是计算平均差的总值(总方差=方差*样本数) + * 二元切分方式 diff --git a/src/python/01.NumPy.py b/src/python/01.NumPy.py index 256b0b93..496dd0fe 100644 --- a/src/python/01.NumPy.py +++ b/src/python/01.NumPy.py @@ -20,10 +20,15 @@ randArray = random.rand(4, 4) # 转化关系， 数组转化为矩阵 randMat = mat(randArray) -# .I表示对矩阵求逆 +# .I表示对矩阵求逆(可以利用矩阵的初等变换 +# # 意义：逆矩阵是一个判断相似性的工具。逆矩阵A与列向量p相乘后，将得到列向量q，q的第i个分量表示p与A的第i个列向量的相似度。 +# # 参考案例链接： +# # https://www.zhihu.com/question/33258489 +# # http://blog.csdn.net/vernice/article/details/48506027 +# .T表示对矩阵转置(行列颠倒) invRandMat = randMat.I # 输出结果 -print randArray, '\n', randMat, '\n', invRandMat +print randArray, '\n---\n', randMat, '\n+++\n', invRandMat # 矩阵和逆矩阵 进行求积 (单位矩阵，对角线都为1嘛，理论上4*4的矩阵其他的都为0) myEye = randMat*invRandMat # 误差 diff --git a/src/python/03.DecisionTree/DTSklearn.py b/src/python/03.DecisionTree/DTSklearn.py index 5155b214..a4890961 100644 --- a/src/python/03.DecisionTree/DTSklearn.py +++ b/src/python/03.DecisionTree/DTSklearn.py @@ -98,6 +98,7 @@ def show_pdf(clf): # from IPython.display import Image # Image(graph.create_png()) + if __name__ == '__main__': x, y = createDataSet() diff --git a/src/python/03.DecisionTree/DecisionTreePlot.py b/src/python/03.DecisionTree/DecisionTreePlot.py index cf11e382..737e4d31 100644 --- a/src/python/03.DecisionTree/DecisionTreePlot.py +++ b/src/python/03.DecisionTree/DecisionTreePlot.py @@ -77,9 +77,9 @@ def plotTree(myTree, parentPt, nodeTxt): plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD for key in secondDict.keys(): # 判断该节点是否是Node节点 - if type(secondDict[key]).__name__=='dict': + if type(secondDict[key]).__name__ == 'dict': # 如果是就递归调用[recursion] - plotTree(secondDict[key],cntrPt,str(key)) + plotTree(secondDict[key], cntrPt, str(key)) else: # 如果不是，就在原来节点一半的地方找到节点的坐标 plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW @@ -121,7 +121,7 @@ def createPlot(inTree): # 测试数据集 def retrieveTree(i): - listOfTrees =[ + listOfTrees = [ {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}}, {'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}} ] diff --git a/src/python/09.RegTrees/TreeNode.py b/src/python/09.RegTrees/TreeNode.py new file mode 100644 index 00000000..c6e76583 --- /dev/null +++ b/src/python/09.RegTrees/TreeNode.py @@ -0,0 +1,16 @@ +#!/usr/bin/python +# coding:utf8 + +''' +Created on 2017-03-06 +Update on 2017-03-06 +@author: jiangzhonglian +''' + + +class treeNode(): + def __init__(self, feat, val, right, left): + self.featureToSplitOn = feat + self.valueOfSplit = val + self.rightBranch = right + self.leftBranch = left diff --git a/src/python/09.RegTrees/regTrees.py b/src/python/09.RegTrees/regTrees.py index c1436e0e..bfe43a74 100644 --- a/src/python/09.RegTrees/regTrees.py +++ b/src/python/09.RegTrees/regTrees.py @@ -9,25 +9,136 @@ Tree-Based Regression Methods Source Code for Machine Learning in Action Ch. 9 ''' from numpy import * -def loadDataSet(fileName): #general function to parse tab -delimited floats - dataMat = [] #assume last column is target value + +# 默认解析的数据是用tab分隔，并且是数值类型 +# general function to parse tab -delimited floats +def loadDataSet(fileName): + """loadDataSet(解析每一行，并转化为float类型) + + Args: + fileName 文件名 + Returns: + dataMat 每一行的数据集array类型 + Raises: + """ + # 假定最后一列是结果值 + # assume last column is target value + dataMat = [] fr = open(fileName) for line in fr.readlines(): curLine = line.strip().split('\t') - fltLine = map(float,curLine) #map all elements to float() + # 将所有的元素转化为float类型 + # map all elements to float() + fltLine = map(float, curLine) dataMat.append(fltLine) return dataMat + def binSplitDataSet(dataSet, feature, value): - mat0 = dataSet[nonzero(dataSet[:,feature] > value)[0],:][0] - mat1 = dataSet[nonzero(dataSet[:,feature] <= value)[0],:][0] - return mat0,mat1 + """binSplitDataSet(将数据集，按照feature列的value进行 二元切分) -def regLeaf(dataSet):#returns the value used for each leaf - return mean(dataSet[:,-1]) + Args: + fileName 文件名 + Returns: + dataMat 每一行的数据集array类型 + Raises: + """ + # # 测试案例 + # print 'dataSet[:, feature]=', dataSet[:, feature] + # print 'nonzero(dataSet[:, feature] > value)[0]=', nonzero(dataSet[:, feature] > value)[0] + # print 'nonzero(dataSet[:, feature] <= value)[0]=', nonzero(dataSet[:, feature] <= value)[0] + # dataSet[:, feature] 取去每一行中，第1列的值(从0开始算) + # nonzero(dataSet[:, feature] > value) 返回结果为true行的index下标 + mat0 = dataSet[nonzero(dataSet[:, feature] > value)[0], :] + mat1 = dataSet[nonzero(dataSet[:, feature] <= value)[0], :] + return mat0, mat1 + + +# 返回每一个叶子结点的均值 +# returns the value used for each leaf +def regLeaf(dataSet): + return mean(dataSet[:, -1]) + + +# 计算总方差=方差*样本数 def regErr(dataSet): - return var(dataSet[:,-1]) * shape(dataSet)[0] + # shape(dataSet)[0] 表示行数 + return var(dataSet[:, -1]) * shape(dataSet)[0] + + +# 1.用最佳方式切分数据集 +# 2.生成相应的叶节点 +def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1, 4)): + """chooseBestSplit(用最佳方式切分数据集 和 生成相应的叶节点) + + Args: + dataSet 数据集 + leafType 计算叶子节点的函数 + errType 求总方差 + ops [容许误差下降值，切分的最少样本数] + Returns: + bestIndex feature的index坐标 + bestValue 切分的最优值 + Raises: + """ + tolS = ops[0] + tolN = ops[1] + # 如果结果集(最后一列为1个变量)，就返回推出 + # .T 对数据集进行转置 + # .tolist()[0] 转化为数组并取第0列 + if len(set(dataSet[:, -1].T.tolist()[0])) == 1: + # exit cond 1 + return None, leafType(dataSet) + # 计算行列值 + m, n = shape(dataSet) + print m, n + # 无分类误差的总方差和 + # the choice of the best feature is driven by Reduction in RSS error from mean + S = errType(dataSet) + # inf 正无穷大 + bestS, bestIndex, bestValue = inf, 0, 0 + # 循环处理每一列对应的feature值 + for featIndex in range(n-1): + for splitVal in set(dataSet[:, featIndex].T.tolist()[0]): + # 对该列进行分组，然后组内的成员的val值进行 二元切分 + mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal) + # 判断二元切分的方式的元素数量是否符合预期 + if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): + continue + newS = errType(mat0) + errType(mat1) + # 如果二元切分，算出来的误差在可接受范围内，那么就记录切分点，并记录最小误差 + if newS < bestS: + bestIndex = featIndex + bestValue = splitVal + bestS = newS + # 判断二元切分的方式的元素误差是否符合预期 + # if the decrease (S-bestS) is less than a threshold don't do the split + if (S - bestS) < tolS: + return None, leafType(dataSet) + mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue) + # 对整体的成员进行判断，是否符合预期 + if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): + return None, leafType(dataSet) + return bestIndex, bestValue + + +# assume dataSet is NumPy Mat so we can array filtering +def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(1, 4)): + # 选择最好的切分方式： feature索引值，最优切分值 + # choose the best split + feat, val = chooseBestSplit(dataSet, leafType, errType, ops) + # if the splitting hit a stop condition return val + if feat is None: + return val + retTree = {} + retTree['spInd'] = feat + retTree['spVal'] = val + lSet, rSet = binSplitDataSet(dataSet, feat, val) + retTree['right'] = createTree(lSet, leafType, errType, ops) + retTree['left'] = createTree(rSet, leafType, errType, ops) + return retTree + def linearSolve(dataSet): #helper function used in two places m,n = shape(dataSet) @@ -49,43 +160,7 @@ def modelErr(dataSet): yHat = X * ws return sum(power(Y - yHat,2)) -def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)): - tolS = ops[0]; tolN = ops[1] - #if all the target variables are the same value: quit and return value - if len(set(dataSet[:,-1].T.tolist()[0])) == 1: #exit cond 1 - return None, leafType(dataSet) - m,n = shape(dataSet) - #the choice of the best feature is driven by Reduction in RSS error from mean - S = errType(dataSet) - bestS = inf; bestIndex = 0; bestValue = 0 - for featIndex in range(n-1): - for splitVal in set(dataSet[:,featIndex]): - mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal) - if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): continue - newS = errType(mat0) + errType(mat1) - if newS < bestS: - bestIndex = featIndex - bestValue = splitVal - bestS = newS - #if the decrease (S-bestS) is less than a threshold don't do the split - if (S - bestS) < tolS: - return None, leafType(dataSet) #exit cond 2 - mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue) - if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): #exit cond 3 - return None, leafType(dataSet) - return bestIndex,bestValue#returns the best feature to split on - #and the value used for that split -def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):#assume dataSet is NumPy Mat so we can array filtering - feat, val = chooseBestSplit(dataSet, leafType, errType, ops)#choose the best split - if feat == None: return val #if the splitting hit a stop condition return val - retTree = {} - retTree['spInd'] = feat - retTree['spVal'] = val - lSet, rSet = binSplitDataSet(dataSet, feat, val) - retTree['left'] = createTree(lSet, leafType, errType, ops) - retTree['right'] = createTree(rSet, leafType, errType, ops) - return retTree def isTree(obj): return (type(obj).__name__=='dict') @@ -137,4 +212,21 @@ def createForeCast(tree, testData, modelEval=regTreeEval): yHat = mat(zeros((m,1))) for i in range(m): yHat[i,0] = treeForeCast(tree, mat(testData[i]), modelEval) - return yHat \ No newline at end of file + return yHat + +if __name__ == "__main__": + + + # # 测试数据集 + # testMat = mat(eye(4)) + # print testMat + # print type(testMat) + # mat0, mat1 = binSplitDataSet(testMat, 1, 0.5) + # print mat0, '\n-----------\n', mat1 + + # 获取数据集 + # myDat = loadDataSet('testData/RT_data1.txt') + myDat = loadDataSet('testData/RT_data2.txt') + myMat = mat(myDat) + myTree = createTree(myMat) + print myTree diff --git a/testData/RT_data1.txt b/testData/RT_data1.txt new file mode 100755 index 00000000..fa98fa58 --- /dev/null +++ b/testData/RT_data1.txt @@ -0,0 +1,200 @@ +0.036098 0.155096 +0.993349 1.077553 +0.530897 0.893462 +0.712386 0.564858 +0.343554 -0.371700 +0.098016 -0.332760 +0.691115 0.834391 +0.091358 0.099935 +0.727098 1.000567 +0.951949 0.945255 +0.768596 0.760219 +0.541314 0.893748 +0.146366 0.034283 +0.673195 0.915077 +0.183510 0.184843 +0.339563 0.206783 +0.517921 1.493586 +0.703755 1.101678 +0.008307 0.069976 +0.243909 -0.029467 +0.306964 -0.177321 +0.036492 0.408155 +0.295511 0.002882 +0.837522 1.229373 +0.202054 -0.087744 +0.919384 1.029889 +0.377201 -0.243550 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0.772495 2.819376 +1.000000 0.784133 2.830665 +1.000000 0.056934 0.234633 +1.000000 0.425584 1.810782 +1.000000 0.998709 4.237235 +1.000000 0.707815 3.034768 +1.000000 0.413816 1.742106 +1.000000 0.217152 1.169250 +1.000000 0.360503 0.831165 +1.000000 0.977989 3.729376 +1.000000 0.507953 1.823205 +1.000000 0.920771 4.021970 +1.000000 0.210542 1.262939 +1.000000 0.928611 4.159518 +1.000000 0.580373 2.039114 +1.000000 0.841390 4.101837 +1.000000 0.681530 2.778672 +1.000000 0.292795 1.228284 +1.000000 0.456918 1.736620 +1.000000 0.134128 -0.195046 +1.000000 0.016241 -0.063215 +1.000000 0.691214 3.305268 +1.000000 0.582002 2.063627 +1.000000 0.303102 0.898840 +1.000000 0.622598 2.701692 +1.000000 0.525024 1.992909 +1.000000 0.996775 3.811393 +1.000000 0.881025 4.353857 +1.000000 0.723457 2.635641 +1.000000 0.676346 2.856311 +1.000000 0.254625 1.352682 +1.000000 0.488632 2.336459 +1.000000 0.519875 2.111651 +1.000000 0.160176 0.121726 +1.000000 0.609483 3.264605 +1.000000 0.531881 2.103446 +1.000000 0.321632 0.896855 +1.000000 0.845148 4.220850 +1.000000 0.012003 -0.217283 +1.000000 0.018883 -0.300577 +1.000000 0.071476 0.006014 From 2f56c4a59bdfd38f0669c714ba9acef47a961983 Mon Sep 17 00:00:00 2001 From: geekidentity Date: Tue, 7 Mar 2017 23:07:24 +0800 Subject: [PATCH 7/9] =?UTF-8?q?change=205.Logistic=E5=9B=9E=E5=BD=92.md=20?= =?UTF-8?q?=20encoding=20to=20utf-8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/5.Logistic回归.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/5.Logistic回归.md b/docs/5.Logistic回归.md index e049ec53..a6c6a445 100644 --- a/docs/5.Logistic回归.md +++ b/docs/5.Logistic回归.md @@ -1,11 +1,11 @@ -# 1) ߼ع +# 1) 逻辑回归基础 - * ߼ع(Logistic Regression) - * 1.1 - * 1.2 ˵ʾ - * 1.3 ж߽ - * 1.4 ۺ - * 1.5 򻯵ijɱݶ½ - * 1.6 ߼Ż - * 1.7 ࣺһ \ No newline at end of file + * 逻辑回归(Logistic Regression) + * 1.1 分类问题 + * 1.2 假说表示 + * 1.3 判定边界 + * 1.4 代价函数 + * 1.5 简化的成本函数和梯度下降 + * 1.6 高级优化 + * 1.7 多类分类：一个对所有 \ No newline at end of file From c53cf4ff4e93ed6402211fb366f8c940180f305f Mon Sep 17 00:00:00 2001 From: jiangzhonglian Date: Wed, 8 Mar 2017 01:09:06 +0800 Subject: [PATCH 8/9] =?UTF-8?q?=E6=B5=8B=E8=AF=95=E5=AE=8C=EF=BC=9A?= =?UTF-8?q?=E5=9B=9E=E5=BD=92=E6=A0=91=20VS=20=E6=A8=A1=E5=9E=8B=E6=A0=91?= =?UTF-8?q?=20VS=20=E7=BA=BF=E6=80=A7=E5=9B=9E=E5=BD=92?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/python/09.RegTrees/regTrees.py | 224 ++++++++++++++++++++-------- src/python/regression.py | 16 +- testData/RT_bikeSpeedVsIq_test.txt | 200 +++++++++++++++++++++++++ testData/RT_bikeSpeedVsIq_train.txt | 200 +++++++++++++++++++++++++ testData/RT_data3.txt | 200 +++++++++++++++++++++++++ testData/RT_data3test.txt | 200 +++++++++++++++++++++++++ testData/RT_data4.txt | 200 +++++++++++++++++++++++++ testData/Regression_data.txt | 200 +++++++++++++++++++++++++ 8 files changed, 1371 insertions(+), 69 deletions(-) create mode 100755 testData/RT_bikeSpeedVsIq_test.txt create mode 100755 testData/RT_bikeSpeedVsIq_train.txt create mode 100755 testData/RT_data3.txt create mode 100755 testData/RT_data3test.txt create mode 100755 testData/RT_data4.txt create mode 100755 testData/Regression_data.txt diff --git a/src/python/09.RegTrees/regTrees.py b/src/python/09.RegTrees/regTrees.py index bfe43a74..11a8be85 100644 --- a/src/python/09.RegTrees/regTrees.py +++ b/src/python/09.RegTrees/regTrees.py @@ -38,9 +38,12 @@ def binSplitDataSet(dataSet, feature, value): """binSplitDataSet(将数据集，按照feature列的value进行 二元切分) Args: - fileName 文件名 + dataMat 数据集 + feature 特征列 + value 特征列要比较的值 Returns: - dataMat 每一行的数据集array类型 + mat0 小于的数据集在左边 + mat1 大于的数据集在右边 Raises: """ # # 测试案例 @@ -50,8 +53,8 @@ def binSplitDataSet(dataSet, feature, value): # dataSet[:, feature] 取去每一行中，第1列的值(从0开始算) # nonzero(dataSet[:, feature] > value) 返回结果为true行的index下标 - mat0 = dataSet[nonzero(dataSet[:, feature] > value)[0], :] - mat1 = dataSet[nonzero(dataSet[:, feature] <= value)[0], :] + mat0 = dataSet[nonzero(dataSet[:, feature] <= value)[0], :] + mat1 = dataSet[nonzero(dataSet[:, feature] > value)[0], :] return mat0, mat1 @@ -92,7 +95,6 @@ def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1, 4)): return None, leafType(dataSet) # 计算行列值 m, n = shape(dataSet) - print m, n # 无分类误差的总方差和 # the choice of the best feature is driven by Reduction in RSS error from mean S = errType(dataSet) @@ -134,89 +136,136 @@ def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(1, 4)): retTree = {} retTree['spInd'] = feat retTree['spVal'] = val + # 大于在右边，小于在左边 lSet, rSet = binSplitDataSet(dataSet, feat, val) - retTree['right'] = createTree(lSet, leafType, errType, ops) - retTree['left'] = createTree(rSet, leafType, errType, ops) + # 递归的进行调用 + retTree['left'] = createTree(lSet, leafType, errType, ops) + retTree['right'] = createTree(rSet, leafType, errType, ops) return retTree -def linearSolve(dataSet): #helper function used in two places - m,n = shape(dataSet) - X = mat(ones((m,n))); Y = mat(ones((m,1)))#create a copy of data with 1 in 0th postion - X[:,1:n] = dataSet[:,0:n-1]; Y = dataSet[:,-1]#and strip out Y - xTx = X.T*X - if linalg.det(xTx) == 0.0: - raise NameError('This matrix is singular, cannot do inverse,\n\ - try increasing the second value of ops') - ws = xTx.I * (X.T * Y) - return ws,X,Y - -def modelLeaf(dataSet):#create linear model and return coeficients - ws,X,Y = linearSolve(dataSet) - return ws - -def modelErr(dataSet): - ws,X,Y = linearSolve(dataSet) - yHat = X * ws - return sum(power(Y - yHat,2)) - - - +# 判断节点是否是一个字典 def isTree(obj): - return (type(obj).__name__=='dict') + return (type(obj).__name__ == 'dict') + +# 计算左右枝丫的均值 def getMean(tree): - if isTree(tree['right']): tree['right'] = getMean(tree['right']) - if isTree(tree['left']): tree['left'] = getMean(tree['left']) + if isTree(tree['right']): + tree['right'] = getMean(tree['right']) + if isTree(tree['left']): + tree['left'] = getMean(tree['left']) return (tree['left']+tree['right'])/2.0 - + + +# 检查是否适合合并分枝 def prune(tree, testData): - if shape(testData)[0] == 0: return getMean(tree) #if we have no test data collapse the tree - if (isTree(tree['right']) or isTree(tree['left'])):#if the branches are not trees try to prune them + # 判断是否测试数据集没有数据 + if shape(testData)[0] == 0: + return getMean(tree) + # 对测试进行分支，看属于哪只分支，然后返回tree结果的均值 + if (isTree(tree['right']) or isTree(tree['left'])): lSet, rSet = binSplitDataSet(testData, tree['spInd'], tree['spVal']) - if isTree(tree['left']): tree['left'] = prune(tree['left'], lSet) - if isTree(tree['right']): tree['right'] = prune(tree['right'], rSet) - #if they are now both leafs, see if we can merge them + if isTree(tree['left']): + tree['left'] = prune(tree['left'], lSet) + if isTree(tree['right']): + tree['right'] = prune(tree['right'], rSet) + + # 如果左右两边无子分支，那么计算一下总方差 和 该结果集的本身不分枝的总方差比较 + # 1.如果测试数据集足够大，将tree进行分支到最后 + # 2.如果测试数据集不够大，那么就无法进行合并 + # 注意返回的结果： 是合并后对原来为字典tree进行赋值，相当于进行了合并 if not isTree(tree['left']) and not isTree(tree['right']): lSet, rSet = binSplitDataSet(testData, tree['spInd'], tree['spVal']) - errorNoMerge = sum(power(lSet[:,-1] - tree['left'],2)) +\ - sum(power(rSet[:,-1] - tree['right'],2)) - treeMean = (tree['left']+tree['right'])/2.0 - errorMerge = sum(power(testData[:,-1] - treeMean,2)) - if errorMerge < errorNoMerge: + # power(x, y)表示x的y次方 + errorNoMerge = sum(power(lSet[:, -1] - tree['left'], 2)) + sum(power(rSet[:, -1] - tree['right'], 2)) + treeMean = (tree['left'] + tree['right'])/2.0 + errorMerge = sum(power(testData[:, -1] - treeMean, 2)) + # 如果 合并的总方差 < 不合并的总方差，那么就进行合并 + if errorMerge < errorNoMerge: print "merging" return treeMean - else: return tree - else: return tree - + else: + return tree + else: + return tree + + +# 得到模型的ws系数：f(x) = x0 + x1*featrue1+ x3*featrue2 ... +# create linear model and return coeficients +def modelLeaf(dataSet): + ws, X, Y = linearSolve(dataSet) + return ws + + +# 计算线性模型的误差值 +def modelErr(dataSet): + ws, X, Y = linearSolve(dataSet) + yHat = X * ws + # print corrcoef(yHat, Y, rowvar=0) + return sum(power(Y - yHat, 2)) + + + # helper function used in two places +def linearSolve(dataSet): + m, n = shape(dataSet) + # 产生一个关于1的矩阵 + X = mat(ones((m, n))) + Y = mat(ones((m, 1))) + # X的0列为1，常数项，用于计算平衡误差 + X[:, 1: n] = dataSet[:, 0: n-1] + Y = dataSet[:, -1] + + # 转置矩阵*矩阵 + xTx = X.T * X + # 如果矩阵的逆不存在，会造成程序异常 + if linalg.det(xTx) == 0.0: + raise NameError('This matrix is singular, cannot do inverse,\ntry increasing the second value of ops') + # 最小二乘法求最优解 + ws = xTx.I * (X.T * Y) + return ws, X, Y + + +# 回归树测试案例 def regTreeEval(model, inDat): return float(model) + +# 模型树测试案例 def modelTreeEval(model, inDat): n = shape(inDat)[1] - X = mat(ones((1,n+1))) - X[:,1:n+1]=inDat - return float(X*model) + X = mat(ones((1, n+1))) + X[:, 1: n+1] = inDat + # print X, model + return float(X * model) + +# 计算预测的结果 def treeForeCast(tree, inData, modelEval=regTreeEval): - if not isTree(tree): return modelEval(tree, inData) - if inData[tree['spInd']] > tree['spVal']: - if isTree(tree['left']): return treeForeCast(tree['left'], inData, modelEval) - else: return modelEval(tree['left'], inData) + if not isTree(tree): + return modelEval(tree, inData) + if inData[tree['spInd']] <= tree['spVal']: + if isTree(tree['left']): + return treeForeCast(tree['left'], inData, modelEval) + else: + return modelEval(tree['left'], inData) else: - if isTree(tree['right']): return treeForeCast(tree['right'], inData, modelEval) - else: return modelEval(tree['right'], inData) - + if isTree(tree['right']): + return treeForeCast(tree['right'], inData, modelEval) + else: + return modelEval(tree['right'], inData) + + +# 预测结果 def createForeCast(tree, testData, modelEval=regTreeEval): - m=len(testData) - yHat = mat(zeros((m,1))) + m = len(testData) + yHat = mat(zeros((m, 1))) for i in range(m): - yHat[i,0] = treeForeCast(tree, mat(testData[i]), modelEval) + yHat[i, 0] = treeForeCast(tree, mat(testData[i]), modelEval) return yHat + if __name__ == "__main__": - - # # 测试数据集 # testMat = mat(eye(4)) # print testMat @@ -224,9 +273,52 @@ if __name__ == "__main__": # mat0, mat1 = binSplitDataSet(testMat, 1, 0.5) # print mat0, '\n-----------\n', mat1 - # 获取数据集 + # 回归树 # myDat = loadDataSet('testData/RT_data1.txt') - myDat = loadDataSet('testData/RT_data2.txt') - myMat = mat(myDat) - myTree = createTree(myMat) - print myTree + # myDat = loadDataSet('testData/RT_data2.txt') + # myMat = mat(myDat) + # myTree = createTree(myMat) + + # 1. 预剪枝就是，提起设置最大误差数和最少元素数 + # myDat = loadDataSet('testData/RT_data3.txt') + # myMat = mat(myDat) + # myTree = createTree(myMat, ops=(0, 1)) + # print myTree + + # 2.后剪枝 + # myDatTest = loadDataSet('testData/RT_data3test.txt') + # myMat2Test = mat(myDatTest) + # myFinalTree = prune(myTree, myMat2Test) + # print '\n\n\n-------------------' + # print myFinalTree + + # -------- + # 模型树求解 + # myDat = loadDataSet('testData/RT_data4.txt') + # myMat = mat(myDat) + # myTree = createTree(myMat, modelLeaf, modelErr) + # print myTree + + # 回归树 VS 模型树 VS 线性回归 + trainMat = mat(loadDataSet('testData/RT_bikeSpeedVsIq_train.txt')) + testMat = mat(loadDataSet('testData/RT_bikeSpeedVsIq_test.txt')) + # 回归树 + myTree1 = createTree(trainMat, ops=(1, 20)) + print myTree1 + yHat1 = createForeCast(myTree1, testMat[:, 0]) + print "回归树:", corrcoef(yHat1, testMat[:, 1],rowvar=0)[0, 1] + + # 模型树 + myTree2 = createTree(trainMat, modelLeaf, modelErr, ops=(1, 20)) + yHat2 = createForeCast(myTree2, testMat[:, 0], modelTreeEval) + print myTree2 + print "模型树:", corrcoef(yHat2, testMat[:, 1],rowvar=0)[0, 1] + + # 线性回归 + ws, X, Y = linearSolve(trainMat) + print ws + m = len(testMat[:, 0]) + yHat3 = mat(zeros((m, 1))) + for i in range(shape(testMat)[0]): + yHat3[i] = testMat[i, 0]*ws[1, 0] + ws[0, 0] + print "线性回归:", corrcoef(yHat3, testMat[:, 1],rowvar=0)[0, 1] diff --git a/src/python/regression.py b/src/python/regression.py index de328368..ab997872 100644 --- a/src/python/regression.py +++ b/src/python/regression.py @@ -11,6 +11,7 @@ import os from numpy import * import matplotlib.pylab as plt + def loadDataSet(fileName): #general function to parse tab -delimited floats numFeat = len(open(fileName).readline().split('\t')) - 1 #get number of fields dataMat = []; labelMat = [] @@ -24,6 +25,7 @@ def loadDataSet(fileName): #general function to parse tab -delimited floats labelMat.append(float(curLine[-1])) return dataMat,labelMat + def standRegres(xArr,yArr): # >>> A.T # transpose, 转置 xMat = mat(xArr); yMat = mat(yArr).T @@ -37,6 +39,7 @@ def standRegres(xArr,yArr): ws = xTx.I * (xMat.T*yMat) # 最小二乘法求最优解 return ws + def plotBestFit(xArr, yArr, ws): xMat = mat(xArr) @@ -60,6 +63,7 @@ def plotBestFit(xArr, yArr, ws): plt.xlabel('X'); plt.ylabel('Y') plt.show() + def main1(): # w0*x0+w1*x1+w2*x2=f(x) project_dir = os.path.dirname(os.path.dirname(os.getcwd())) @@ -91,6 +95,7 @@ def lwlr(testPoint, xArr, yArr,k=1.0): ws = xTx.I * (xMat.T * (weights * yMat)) return testPoint * ws + def lwlrTest(testArr,xArr,yArr,k=1.0): #loops over all the data points and applies lwlr to each one m = shape(testArr)[0] # m*1的矩阵 @@ -101,6 +106,7 @@ def lwlrTest(testArr,xArr,yArr,k=1.0): #loops over all the data points and appl yHat[i] = lwlr(testArr[i],xArr,yArr,k) return yHat + def lwlrTestPlot(xArr, yArr, yHat): xMat = mat(xArr) @@ -123,11 +129,13 @@ def lwlrTestPlot(xArr, yArr, yHat): plt.xlabel('X'); plt.ylabel('Y') plt.show() + def main2(): # w0*x0+w1*x1+w2*x2=f(x) - project_dir = os.path.dirname(os.path.dirname(os.getcwd())) + # project_dir = os.path.dirname(os.path.dirname(os.getcwd())) # 1.收集并准备数据 - xArr, yArr = loadDataSet("%s/resources/ex0.txt" % project_dir) + # xArr, yArr = loadDataSet("%s/resources/ex0.txt" % project_dir) + xArr, yArr = loadDataSet("testData/Regression_data.txt") # print xArr, '---\n', yArr # 2.训练模型， f(x)=a1*x1+b2*x2+..+nn*xn中 (a1,b2, .., nn).T的矩阵值 yHat = lwlrTest(xArr, xArr, yArr, 0.003) @@ -136,12 +144,14 @@ def main2(): # 数据可视化 lwlrTestPlot(xArr, yArr, yHat) -if __name__=="__main__": + +if __name__ == "__main__": # 线性回归 # main1() # 局部加权线性回归 main2() + def rssError(yArr,yHatArr): #yArr and yHatArr both need to be arrays return ((yArr-yHatArr)**2).sum() diff --git a/testData/RT_bikeSpeedVsIq_test.txt b/testData/RT_bikeSpeedVsIq_test.txt new file mode 100755 index 00000000..9fbf3e1f --- /dev/null +++ b/testData/RT_bikeSpeedVsIq_test.txt @@ -0,0 +1,200 @@ +12.000000 121.010516 +19.000000 157.337044 +12.000000 116.031825 +15.000000 132.124872 +2.000000 52.719612 +6.000000 39.058368 +3.000000 50.757763 +20.000000 166.740333 +11.000000 115.808227 +21.000000 165.582995 +3.000000 41.956087 +3.000000 34.432370 +13.000000 116.954676 +1.000000 32.112553 +7.000000 50.380243 +7.000000 94.107791 +23.000000 188.943179 +18.000000 152.637773 +9.000000 104.122082 +18.000000 127.805226 +0.000000 83.083232 +15.000000 148.180104 +3.000000 38.480247 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0.918916 4.632025 +1.000000 0.441815 3.756750 +1.000000 0.116598 3.133555 +1.000000 0.359274 3.567919 +1.000000 0.814811 4.363382 +1.000000 0.387125 3.560165 +1.000000 0.982243 4.564305 +1.000000 0.780880 4.215055 +1.000000 0.652565 4.174999 +1.000000 0.870030 4.586640 +1.000000 0.604755 3.960008 +1.000000 0.255212 3.529963 +1.000000 0.730546 4.213412 +1.000000 0.493829 3.908685 +1.000000 0.257017 3.585821 +1.000000 0.833735 4.374394 +1.000000 0.070095 3.213817 +1.000000 0.527070 3.952681 +1.000000 0.116163 3.129283 From 707feca941a249d134c262f39cd4e3af07bb71b2 Mon Sep 17 00:00:00 2001 From: geekidentity Date: Wed, 8 Mar 2017 08:30:24 +0800 Subject: [PATCH 9/9] =?UTF-8?q?=E6=9C=BA=E5=99=A8=E5=AD=A6=E4=B9=A0?= =?UTF-8?q?=E7=9A=84=E4=BB=BB=E5=8A=A1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/1.机器学习基础.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/1.机器学习基础.md b/docs/1.机器学习基础.md index d4e74a54..ee1c06e6 100644 --- a/docs/1.机器学习基础.md +++ b/docs/1.机器学习基础.md @@ -5,6 +5,8 @@ * 把无序的数据转换成有用的信息。 * 机器学习的意义 * 我们利用计算机来彰显数据背后的真实含义。 +* 机器学习的任务 + * 机器学习的主要任务就是分类。 * 监督学习 * 样本集：训练数据 + 测试数据 * 特征(feature-是否有缺失情况) + 目标变量(分类-离散值/回归-连续值<0~100、 -999～999>)