From 20bf00bc9c7bf46ea3053ea897e1489177779218 Mon Sep 17 00:00:00 2001 From: aolingwen <747620155@qq.com> Date: Tue, 9 Jul 2019 14:56:48 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B91?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- AGNES.md | 4 +- algorithm.md | 4 +- bayes.md | 14 ++--- classification_metrics.md | 20 ++++---- cluster_metrics.md | 6 +-- decision_tree.md | 6 +-- img/10.jpg | Bin 7904 -> 10787 bytes img/11.jpg | Bin 6734 -> 14840 bytes img/12.jpg | Bin 6598 -> 14965 bytes img/13.jpg | Bin 6692 -> 15102 bytes img/14.jpg | Bin 13620 -> 37681 bytes img/16.jpg | Bin 24554 -> 42250 bytes img/17.jpg | Bin 61482 -> 203210 bytes img/21.jpg | Bin 11935 -> 24976 bytes kMeans.md | 2 +- kNN.md | 10 ++-- linear_regression.md | 22 ++++---- logistic_regression.md | 12 ++--- machine_learning.md | 2 +- multi-class-learning.md | 2 +- pingpong/Policy Gradient.md | 2 +- pingpong/what is reinforce learning.md | 2 +- ppt/机器学习.ppt | Bin 0 -> 20992 bytes random_forest.md | 6 +-- sklearn.md | 36 ++++++------- some term.md | 2 +- svm.md | 68 +++++++++++++------------ titanic/EDA.md | 6 +-- titanic/feature engerning.md | 2 +- titanic/tuning.md | 2 +- what's machine_learning.md | 4 +- work flow.md | 10 ++-- 32 files changed, 124 insertions(+), 120 deletions(-) create mode 100644 ppt/机器学习.ppt diff --git a/AGNES.md b/AGNES.md index 17cc77b..1f05b7a 100644 --- a/AGNES.md +++ b/AGNES.md @@ -1,4 +1,4 @@ -# 以距离为尺-AGNES算法 +# AGNES算法 `AGNES`算法是一种聚类算法,最初将每个对象作为一个簇,然后这些簇根据某些距离准则被一步步地合并。两个簇间的相似度有多种不同的计算方法。聚类的合并过程反复进行直到所有的对象最终满足簇数目。所以理解`AGNES`算法前需要先理解一些距离准则。 @@ -44,7 +44,7 @@ 举个例子,现在先要将西瓜数据聚成两类,数据如下表所示: | 编号 | 体积 | 重量 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-:| | 1 | 1.2 | 2.3 | | 2 | 3.6 | 7.1 | | 3 | 1.1 | 2.2 | diff --git a/algorithm.md b/algorithm.md index e7c667b..80373d8 100644 --- a/algorithm.md +++ b/algorithm.md @@ -15,6 +15,8 @@ - AGNES -本章的所有实训已在`educoder`平台上提供,若您感兴趣可以通过扫码查看整套课程。 +本章的所有实训已在`educoder`平台上提供,若感兴趣可以输入链接进行体验:https://www.educoder.net/paths/194 + +也通过扫码查看整套课程。
\ No newline at end of file diff --git a/bayes.md b/bayes.md index 909b677..af0fa10 100644 --- a/bayes.md +++ b/bayes.md @@ -1,4 +1,4 @@ -# 用概率说话-朴素贝叶斯分类器 +# 朴素贝叶斯分类器 朴素贝叶斯分类算法是基于贝叶斯理论和特征条件独立假设的分类算法。对于给定的训练集,首先基于特征条件独立假设学习数据的概率分布。然后基于此模型,对于给定的特征数据`x`,利用贝叶斯定理计算出标签`y`。朴素贝叶斯分类算法实现简单,预测的效率很高,是一种常用的分类算法。 @@ -25,7 +25,7 @@ 举个例子,**现在有一个表格,表格中统计了甲乙两个厂生产的产品中合格品数量、次品数量的数据。数据如下:** | | 甲厂 |乙厂 |合计 | -| ------------ | ------------ | ------------ | ------------ | +| :-: | :-: | :-:| :-: | | 合格品 | 475 | 644 | 1119 | | 次品| 25 | 56 | 81 | | 合计| 500 | 700 | 1200 | @@ -58,13 +58,13 @@ 但是每条路每天拥堵的可能性不太一样,由于路的远近不同,选择每条路的概率如下表所示: | L1 | L2 | L3 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 0.5 | 0.3 | 0.2 | 每天从上述三条路去公司时不堵车的概率如下表所示: | L1不堵车 | L2不堵车 | L3不堵车 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 0.2 | 0.4 | 0.7 | 如果不堵车就不会迟到,现在小明想要算一算去公司上班不会迟到的概率是多少,应该怎么办呢? @@ -129,7 +129,7 @@ $$ 假如现在一个西瓜的数据如下表所示: | 颜色 | 声音 | 纹理 | 是否为好瓜 | -| ------------ | ------------ | ------------ | ------------ | +| :-: | :-: | :-: | :-: | | 绿 | 清脆 | 清晰 | ? | 若想使用朴素贝叶斯分类算法的思想,根据这条数据中`颜色`、`声音`和`纹理`这三个特征来推断是不是好瓜,我们需要计算出这个西瓜是好瓜的概率和不是好瓜的概率。 @@ -180,7 +180,7 @@ $$ 训练的流程非常简单,主要是计算各种**条件概率**。假设现在有一组西瓜的数据,如下表所示: | 编号 | 颜色 | 声音 | 纹理 | 是否为好瓜 | -| ------------ | ------------ | ------------ | ------------ | ------------ | +| :-: | :-: | :-: | :-: | :-: | | 1 | 绿 | 清脆 | 清晰 | 是 | | 2 | 黄 | 浑厚 | 模糊 | 否 | | 3 | 绿 | 浑厚 | 模糊 | 是 | @@ -208,7 +208,7 @@ $$ 当得到以上概率后,训练阶段的任务就已经完成了。我们不妨再回过头来预测一下这个西瓜是不是好瓜。 | 颜色 | 声音 | 纹理 | 是否为好瓜 | -| ------------ | ------------ | ------------ | ------------ | +| :-: | :-: | :-: | :-: | | 绿 | 清脆 | 清晰 | ? | 假设事件`A1`为好瓜,事件`B`为绿,事件`C`为清脆,事件`D`为清晰。则有: diff --git a/classification_metrics.md b/classification_metrics.md index efb83ac..53b60a4 100644 --- a/classification_metrics.md +++ b/classification_metrics.md @@ -6,7 +6,7 @@ 准确度这个概念相信对于大家来说肯定并不陌生,就是正确率。例如模型的预测结果与数据真实结果如下表所示: | 编号 | 预测结果 | 真实结果 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 1 | 1 | 2 | | 2 | 2 | 2 | | 3 | 3 | 3 | @@ -33,21 +33,21 @@ 如果我们把这些结果组成如下矩阵,则该矩阵就成为**混淆矩阵**。 | 真实\预测 | 0 | 1 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 0 | 9978 | 12 | | 1 | 2 | 8 | 混淆矩阵中每个格子所代表的的意义也很明显,意义如下: | 真实\预测 | 0 | 1 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 0 | 预测 0 正确的数量 | 预测 1 错误的数量 | | 1 | 预测 0 错误的数量 | 预测 1 正确的数量 | 如果将正确看成是`True`,错误看成是`False`, `0`看成是 `Negtive`,`1`看成是`Positive`。然后将上表中的文字替换掉,混淆矩阵如下: | 真实\预测 | 0 | 1 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 0 | TN | FP | | 1 | FN | TP | @@ -56,7 +56,7 @@ 很明显,当`FN`和`FP`都等于`0`时,模型的性能应该是最好的,因为模型并没有在预测的时候犯错误。即如下混淆矩阵: | 真实\预测 | 0 | 1 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 0 | 9978 | 0 | | 1 | 0 | 22 | @@ -76,7 +76,7 @@ $$ 假如癌症检测系统的混淆矩阵如下: | 真实\预测 | 0 | 1 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 0 | 9978 | 12 | | 1 | 2 | 8 | @@ -99,7 +99,7 @@ $$ 假如癌症检测系统的混淆矩阵如下: | 真实\预测 | 0 | 1 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 0 | 9978 | 12 | | 1 | 2 | 8 | @@ -180,7 +180,7 @@ $$ 从图中可以看出,**当模型的 TPR 越高 FPR 也会越高, TPR 越低 FPR 也会越低。这与精准率和召回率之间的关系刚好相反。**并且,模型的分类阈值一但改变,就有一组对应的`TPR`与`FPR`。假设该模型在不同的分类阈值下其对应的`TPR`与`FPR`如下表所示: | TPR | FPR | -| ------------ | ------------ | +| :-: | :-: | | 0.2 | 0.08 | | 0.35 | 0.1 | | 0.37 | 0.111 | @@ -219,7 +219,7 @@ $$ 举个例子,现有预测概率与真实类别的表格如下所示(其中`0`表示 `Negtive` ,`1`表示`Positive`): | 编号 | 预测概率 | 真实类别 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 1 | 0.1 | 0 | | 2 | 0.4 | 0 | | 3 | 0.3 | 1 | @@ -228,7 +228,7 @@ $$ 想要得到公式中的`rank` ,就需要将预测概率从小到大排序,排序后如下: | 编号 | 预测概率 | 真实类别 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 1 | 0.1 | 0 | | 3 | 0.3 | 1 | | 2 | 0.4 | 0 | diff --git a/cluster_metrics.md b/cluster_metrics.md index d2844fb..4d81ef4 100644 --- a/cluster_metrics.md +++ b/cluster_metrics.md @@ -39,7 +39,7 @@ $$ 举个例子,参考模型给出的簇与聚类模型给出的簇划分如下: | 编号 | 参考簇 | 聚类簇 | -| ------------ | ------------ | ------------ | +| :-: | :-: | :-: | | 1 | 0 | 0 | | 2 | 0 | 0 | | 3 | 0 | 1 | @@ -115,7 +115,7 @@ $$ 举个例子,现在有$$6$$条西瓜数据$$\{x_1,x_2,...,x_6\}$$,这些数据已经聚类成了$$2$$个簇。 | 编号 | 体积 | 重量 | 簇 | -| ------------ | ------------ | ------------ | ------------ | +| :-: | :-: | :-: | :-: | | 1 | 3 | 4 | 1 | | 2 | 6 | 9 | 2 | | 3 | 2 | 3 | 1 | @@ -196,7 +196,7 @@ $$ 还是这个例子,现在有`6`条西瓜数据$$\{x_1,x_2,...,x_6\}$$,这些数据已经聚类成了 2 个簇。 | 编号 | 体积 | 重量 | 簇 | -| ------------ | ------------ | ------------ | ------------ | +| :-: | :-: | :-: | :-: | | 1 | 3 | 4 | 1 | | 2 | 6 | 9 | 2 | | 3 | 2 | 3 | 1 | diff --git a/decision_tree.md b/decision_tree.md index c132154..504c012 100644 --- a/decision_tree.md +++ b/decision_tree.md @@ -1,4 +1,4 @@ -# 最接近人类思维的分类算法-决策树 +# 决策树 ## 什么是决策树 @@ -33,11 +33,11 @@ $$ **当然条件熵的一个性质也熵的性质一样,我概率越确定,条件熵就越小,概率越五五开,条件熵就越大**。 -现在已经知道了什么是熵,什么是条件熵。接下来就可以看看什么是信息增益了。所谓的信息增益就是表示我已知条件 $$X$$ 后能得到信息 $$Y$$ 的不确定性的减少程度。就好比,我在玩读心术。您心里想一件东西,我来猜。我已开始什么都没问你,我要猜的话,肯定是瞎猜。这个时候我的熵就非常高对不对。然后我接下来我会去试着问你是非题,当我问了是非题之后,我就能减小猜测你心中想到的东西的范围,这样其实就是减小了我的熵。那么我熵的减小程度就是我的**信息增益**。 +现在已经知道了什么是熵,什么是条件熵。接下来就可以看看什么是信息增益了。所谓的信息增益就是表示我已知条件 $$X$$ 后能得到信息 $$Y$$ 的不确定性的减少程度。就好比,我在玩读心术。您心里想一件东西,我来猜。我一开始什么都没问你,我要猜的话,肯定是瞎猜。这个时候我的熵就非常高对不对。然后我接下来我会去试着问你是非题,当我问了是非题之后,我就能减小猜测你心中想到的东西的范围,这样其实就是减小了我的熵。那么我熵的减小程度就是我的**信息增益**。 所以信息增益如果套上机器学习的话就是,如果把特征 $$A$$ 对训练集 $$D$$ 的信息增益记为 $$g(D, A)$$ 的话,那么 $$g(D, A)$$ 的计算公式就是:$$g(D,A)=H(D)-H(D|A)$$。 -如果看到这一堆公式可能会懵逼,那不如举个栗子来看看信息增益怎么算。假设我现在有这一个数据表,第一列是性别,第二列是活跃度, 第三列是客户是否流失的 $$label$$。 +如果看到这一堆公式可能会比较烦,那不如举个栗子来看看信息增益怎么算。假设我现在有这一个数据表,第一列是性别,第二列是活跃度, 第三列是客户是否流失的 $$label$$。 diff --git a/img/10.jpg b/img/10.jpg index 7e391c88005c0a96e9915e11fe82e62a1538f172..0dbd7e9e671d39d2149e4ff96973e4174ab484e2 100644 GIT binary patch literal 10787 zcmeHN2{@E{+keK8y|IiXVhSNb7%9n+kYsD2EF(z`VzTeXNfAOQO2WA^RxxbiOGDAZkY? zy_$f8hAdfCQ1)c1+!2k_i-e0hpWn8}7|iUBe6t`*-)P9CZ}BI}tqBgXGXS8s_DBpc 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