P2项目，寻找潜在的捐助者

机器学习纳米学位

监督学习

项目2: 为CharityML寻找捐献者

欢迎来到机器学习工程师纳米学位的第二个项目！在此文件中，有些示例代码已经提供给你，但你还需要实现更多的功能让项目成功运行。除非有明确要求，你无须修改任何已给出的代码。以'练习'开始的标题表示接下来的代码部分中有你必须要实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以'TODO'标出。请仔细阅读所有的提示！

除了实现代码外，你还必须回答一些与项目和你的实现有关的问题。每一个需要你回答的问题都会以'问题 X'为标题。请仔细阅读每个问题，并且在问题后的'回答'文字框中写出完整的答案。我们将根据你对问题的回答和撰写代码所实现的功能来对你提交的项目进行评分。

提示：Code 和 Markdown 区域可通过Shift + Enter快捷键运行。此外，Markdown可以通过双击进入编辑模式。

开始

在这个项目中，你将使用1994年美国人口普查收集的数据，选用几个监督学习算法以准确地建模被调查者的收入。然后，你将根据初步结果从中选择出最佳的候选算法，并进一步优化该算法以最好地建模这些数据。你的目标是建立一个能够准确地预测被调查者年收入是否超过50000美元的模型。这种类型的任务会出现在那些依赖于捐款而存在的非营利性组织。了解人群的收入情况可以帮助一个非营利性的机构更好地了解他们要多大的捐赠，或是否他们应该接触这些人。虽然我们很难直接从公开的资源中推断出一个人的一般收入阶层，但是我们可以（也正是我们将要做的）从其他的一些公开的可获得的资源中获得一些特征从而推断出该值。

这个项目的数据集来自UCI机器学习知识库。这个数据集是由Ron Kohavi和Barry Becker在发表文章"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid"之后捐赠的，你可以在Ron Kohavi提供的在线版本中找到这个文章。我们在这里探索的数据集相比于原有的数据集有一些小小的改变，比如说移除了特征'fnlwgt' 以及一些遗失的或者是格式不正确的记录。

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探索数据

运行下面的代码单元以载入需要的Python库并导入人口普查数据。注意数据集的最后一列'income'将是我们需要预测的列（表示被调查者的年收入会大于或者是最多50,000美元），人口普查数据中的每一列都将是关于被调查者的特征。

In [58]:

# 检查你的Python版本from sys import version_infoif version_info.major != 2 and version_info.minor != 7:    raise Exception('请使用Python 2.7来完成此项目')

In [59]:

# 为这个项目导入需要的库import numpy as npimport pandas as pdfrom time import timefrom IPython.display import display # 允许为DataFrame使用display()# 导入附加的可视化代码visuals.pyimport visuals as vs# 为notebook提供更加漂亮的可视化%matplotlib inline# 导入人口普查数据data = pd.read_csv("census.csv")# 成功 - 显示第一条记录display(data.head(n=5))

 ageworkclasseducation_leveleducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countryincome039State-govBachelors13.0Never-marriedAdm-clericalNot-in-familyWhiteMale2174.00.040.0United-States<=50K150Self-emp-not-incBachelors13.0Married-civ-spouseExec-managerialHusbandWhiteMale0.00.013.0United-States<=50K238PrivateHS-grad9.0DivorcedHandlers-cleanersNot-in-familyWhiteMale0.00.040.0United-States<=50K353Private11th7.0Married-civ-spouseHandlers-cleanersHusbandBlackMale0.00.040.0United-States<=50K428PrivateBachelors13.0Married-civ-spouseProf-specialtyWifeBlackFemale0.00.040.0Cuba<=50K

练习：数据探索

首先我们对数据集进行一个粗略的探索，我们将看看每一个类别里会有多少被调查者？并且告诉我们这些里面多大比例是年收入大于50,000美元的。在下面的代码单元中，你将需要计算以下量：

• 总的记录数量，'n_records'
• 年收入大于50,000美元的人数，'n_greater_50k'.
• 年收入最多为50,000美元的人数 'n_at_most_50k'.
• 年收入大于50,000美元的人所占的比例， 'greater_percent'.

提示： 您可能需要查看上面的生成的表，以了解'income'条目的格式是什么样的。

In [60]:

# TODO：总的记录数n_records = len(data)# TODO：被调查者的收入大于$50,000的人数n_greater_50k = len(data.loc[data["income"] !="<=50K" ])# TODO：被调查者的收入最多为$50,000的人数n_at_most_50k = len(data.loc[data["income"] =="<=50K" ])# TODO：被调查者收入大于$50,000所占的比例greater_percent = 100*float(n_greater_50k)/float(n_records)# 打印结果print "Total number of records: {}".format(n_records)print "Individuals making more than $50,000: {}".format(n_greater_50k)print "Individuals making at most $50,000: {}".format(n_at_most_50k)print "Percentage of individuals making more than $50,000: {:.2f}%".format(greater_percent)

Total number of records: 45222Individuals making more than $50,000: 11208Individuals making at most $50,000: 34014Percentage of individuals making more than $50,000: 24.78%

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准备数据

在数据能够被作为输入提供给机器学习算法之前，它经常需要被清洗，格式化，和重新组织 - 这通常被叫做预处理。幸运的是，对于这个数据集，没有我们必须处理的无效或丢失的条目，然而，由于某一些特征存在的特性我们必须进行一定的调整。这个预处理都可以极大地帮助我们提升几乎所有的学习算法的结果和预测能力。

获得特征和标签

income 列是我们需要的标签，记录一个人的年收入是否高于50K。 因此我们应该把他从数据中剥离出来，单独存放。

In [61]:

# 将数据切分成特征和对应的标签income_raw = data['income']features_raw = data.drop('income', axis = 1)

转换倾斜的连续特征

一个数据集有时可能包含至少一个靠近某个数字的特征，但有时也会有一些相对来说存在极大值或者极小值的不平凡分布的的特征。算法对这种分布的数据会十分敏感，并且如果这种数据没有能够很好地规一化处理会使得算法表现不佳。在人口普查数据集的两个特征符合这个描述：'capital-gain'和'capital-loss'。

运行下面的代码单元以创建一个关于这两个特征的条形图。请注意当前的值的范围和它们是如何分布的。

In [62]:

# 可视化 'capital-gain'和'capital-loss' 两个特征vs.distribution(features_raw)

对于高度倾斜分布的特征如'capital-gain'和'capital-loss'，常见的做法是对数据施加一个对数转换，将数据转换成对数，这样非常大和非常小的值不会对学习算法产生负面的影响。并且使用对数变换显著降低了由于异常值所造成的数据范围异常。但是在应用这个变换时必须小心：因为0的对数是没有定义的，所以我们必须先将数据处理成一个比0稍微大一点的数以成功完成对数转换。

运行下面的代码单元来执行数据的转换和可视化结果。再次，注意值的范围和它们是如何分布的。

In [63]:

# 对于倾斜的数据使用Log转换skewed = ['capital-gain', 'capital-loss']features_raw[skewed] = data[skewed].apply(lambda x: np.log(x + 1))# 可视化对数转换后 'capital-gain'和'capital-loss' 两个特征vs.distribution(features_raw, transformed = True)

规一化数字特征

除了对于高度倾斜的特征施加转换，对数值特征施加一些形式的缩放通常会是一个好的习惯。在数据上面施加一个缩放并不会改变数据分布的形式（比如上面说的'capital-gain' or 'capital-loss'）；但是，规一化保证了每一个特征在使用监督学习器的时候能够被平等的对待。注意一旦使用了缩放，观察数据的原始形式不再具有它本来的意义了，就像下面的例子展示的。

运行下面的代码单元来规一化每一个数字特征。我们将使用sklearn.preprocessing.MinMaxScaler来完成这个任务。

In [64]:

from sklearn.preprocessing import MinMaxScaler# 初始化一个 scaler，并将它施加到特征上scaler = MinMaxScaler()numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']features_raw[numerical] = scaler.fit_transform(data[numerical])# 显示一个经过缩放的样例记录display(features_raw.head(n = 1))

 ageworkclasseducation_leveleducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-country00.30137State-govBachelors0.8Never-marriedAdm-clericalNot-in-familyWhiteMale0.021740.00.397959United-States

练习：数据预处理

从上面的数据探索中的表中，我们可以看到有几个属性的每一条记录都是非数字的。通常情况下，学习算法期望输入是数字的，这要求非数字的特征（称为类别变量）被转换。转换类别变量的一种流行的方法是使用独热编码方案。独热编码为每一个非数字特征的每一个可能的类别创建一个“虚拟”变量。例如，假设someFeature有三个可能的取值A，B或者C，。我们将把这个特征编码成someFeature_A, someFeature_B和someFeature_C.

特征X 特征X_A特征X_B特征X_CB 010C----> 独热编码 ---->001A 100

此外，对于非数字的特征，我们需要将非数字的标签'income'转换成数值以保证学习算法能够正常工作。因为这个标签只有两种可能的类别（"<=50K"和">50K"），我们不必要使用独热编码，可以直接将他们编码分别成两个类0和1，在下面的代码单元中你将实现以下功能：

• 使用pandas.get_dummies()对'features_raw'数据来施加一个独热编码。
• 将目标标签'income_raw'转换成数字项。
  • 将"<=50K"转换成0；将">50K"转换成1。

In [65]:

# TODO：使用pandas.get_dummies()对'features_raw'数据进行独热编码features = pd.get_dummies(features_raw)# TODO：将'income_raw'编码成数字值income = income_raw.replace(['>50K', '<=50K'], [1, 0])# 打印经过独热编码之后的特征数量encoded = list(features.columns)print "{} total features after one-hot encoding.".format(len(encoded))# 移除下面一行的注释以观察编码的特征名字#print encodedprint list(income)

103 total features after one-hot encoding.[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 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混洗和切分数据

现在所有的 类别变量 已被转换成数值特征，而且所有的数值特征已被规一化。和我们一般情况下做的一样，我们现在将数据（包括特征和它们的标签）切分成训练和测试集。其中80%的数据将用于训练和20%的数据用于测试。然后再进一步把训练数据分为训练集和验证集，用来选择和优化模型。

运行下面的代码单元来完成切分。

In [66]:

# 导入 train_test_splitfrom sklearn.model_selection import train_test_split# 将'features'和'income'数据切分成训练集和测试集X_train, X_test, y_train, y_test = train_test_split(features, income, test_size = 0.2, random_state = 0,                                                    stratify = income)# 将'X_train'和'y_train'进一步切分为训练集和验证集X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=0,                                                    stratify = y_train)# 显示切分的结果print "Training set has {} samples.".format(X_train.shape[0])print "Validation set has {} samples.".format(X_val.shape[0])print "Testing set has {} samples.".format(X_test.shape[0])

Training set has 28941 samples.Validation set has 7236 samples.Testing set has 9045 samples.

---

评价模型性能

在这一部分中，我们将尝试四种不同的算法，并确定哪一个能够最好地建模数据。四种算法包含一个天真的预测器 和三个你选择的监督学习器。

评价方法和朴素的预测器

CharityML通过他们的研究人员知道被调查者的年收入大于$50,000最有可能向他们捐款。因为这个原因*CharityML*对于准确预测谁能够获得$50,000以上收入尤其有兴趣。这样看起来使用准确率作为评价模型的标准是合适的。另外，把没有收入大于$50,000的人识别成年收入大于$50,000对于CharityML来说是有害的，因为他想要找到的是有意愿捐款的用户。这样，我们期望的模型具有准确预测那些能够年收入大于$50,000的能力比模型去查全这些被调查者更重要。我们能够使用F-beta score作为评价指标，这样能够同时考虑查准率和查全率：

Fβ=(1+β2)⋅precision⋅recall(β2⋅precision)+recallFβ=(1+β2)⋅precision⋅recall(β2⋅precision)+recall

尤其是，当 β=0.5β=0.5 的时候更多的强调查准率，这叫做F0.50.5 score （或者为了简单叫做F-score）。

问题 1 - 天真的预测器的性能

通过查看收入超过和不超过 $50,000 的人数，我们能发现多数被调查者年收入没有超过 $50,000。如果我们简单地预测说“这个人的收入没有超过 $50,000”，我们就可以得到一个 准确率超过 50% 的预测。这样我们甚至不用看数据就能做到一个准确率超过 50%。这样一个预测被称作是天真的。通常对数据使用一个天真的预测器是十分重要的，这样能够帮助建立一个模型表现是否好的基准。 使用下面的代码单元计算天真的预测器的相关性能。将你的计算结果赋值给'accuracy', ‘precision’, ‘recall’ 和 'fscore'，这些值会在后面被使用，请注意这里不能使用scikit-learn，你需要根据公式自己实现相关计算。

如果我们选择一个无论什么情况都预测被调查者年收入大于 $50,000 的模型，那么这个模型在验证集上的准确率，查准率，查全率和 F-score是多少？

In [72]:

#不能使用scikit-learn，你需要根据公式自己实现相关计算。#TODO： 计算准确率accuracy = float(len(y_val.loc[y_val == 1 ]))/len(y_val)# TODO： 计算查准率 Precisionprecision =float(len(y_val.loc[y_val == 1 ]))/len(y_val)# TODO： 计算查全率 Recallrecall = 1# TODO： 使用上面的公式，设置beta=0.5，计算F-scorefscore = (1+0.5*0.5)*(precision*recall)/((0.5*0.5*precision)+recall)# 打印结果print "Naive Predictor on validation data: \n \    Accuracy score: {:.4f} \n \    Precision: {:.4f} \n \    Recall: {:.4f} \n \    F-score: {:.4f}".format(accuracy, precision, recall, fscore)

Naive Predictor on validation data:      Accuracy score: 0.2478      Precision: 0.2478      Recall: 1.0000      F-score: 0.2917

监督学习模型

问题 2 - 模型应用

你能够在 scikit-learn 中选择以下监督学习模型

• 高斯朴素贝叶斯 (GaussianNB)
• 决策树 (DecisionTree)
• 集成方法 (Bagging, AdaBoost, Random Forest, Gradient Boosting)
• K近邻 (K Nearest Neighbors)
• 随机梯度下降分类器 (SGDC)
• 支撑向量机 (SVM)
• Logistic回归（LogisticRegression）

从上面的监督学习模型中选择三个适合我们这个问题的模型，并回答相应问题。

模型1

模型名称

回答：逻辑回归

描述一个该模型在真实世界的一个应用场景。（你需要为此做点研究，并给出你的引用出处）

回答：预测用户对某件商品的购买偏好，如预测用户在未来某个时间段是否会购买某个品类，如果把会购买标记为1，不会购买标记为0，就转换为一个二分类问题。其中用户特征包括：购买频次，浏览频次，时间，地理位置;品类特征包括：销量，购买用户，浏览用户；其他特征包括：购买频次，浏览频次，购买间隔等；最终预测结果为用户在未来某个时间段对某一类商品的购买概率。http://blog.jobbole.com/88521/

这个模型的优势是什么？他什么情况下表现最好？

回答：简单高效，易于实现，速度快。在特征没有相关性，最终分类结果是线性的，且特征维度远小于数据量的时候效果好。

这个模型的缺点是什么？什么条件下它表现很差？

回答：如果特征相关性比较强，表现就很差。

根据我们当前数据集的特点，为什么这个模型适合这个问题。

回答：是个二分类问题，特征基本不相关，数据量远大于特征数。

模型2

模型名称

回答：集成方法

描述一个该模型在真实世界的一个应用场景。（你需要为此做点研究，并给出你的引用出处）

回答：金融风险控制。比如根据用户在各APP、互联网的浏览数据、社会信用数据、家庭情况数据、个人情况数据等等特征分析是否向其发放贷款。

这个模型的优势是什么？他什么情况下表现最好？

回答：准确率高，实现简单，适应性强。数据维度相对低，同时对准确性有较高要求时，这种集成学习方法有较好效果。

这个模型的缺点是什么？什么条件下它表现很差？

回答：适应性很强，对outlier敏感，容易过拟合。

根据我们当前数据集的特点，为什么这个模型适合这个问题。

回答：这是个分类问题，基本对于集成学习而言，都可以试试。

模型3

模型名称

回答：SVM

描述一个该模型在真实世界的一个应用场景。（你需要为此做点研究，并给出你的引用出处）

回答：视频字幕蕴含了丰富语义，可用于对相应视频 流进行高级语义标注。庄越挺等提出并实践了基于SVM的视频字幕自动定位和提取的方法。该方法首先将原始图像帧分割为N*N 的子 块，提取每个子块的灰度特征；然后使用预先训练好的SVM分类机进行字幕子块和非字幕子块的分类；最后结合金字塔模型和后期处理过程，实现视频图像字幕区域的自动定位提取。实验表明该方法取得了 良好的效果。 https://wenku.baidu.com/view/ad8e3461bb4cf7ec4bfed066.html

这个模型的优势是什么？他什么情况下表现最好？

回答：1.可用于线性/非线性分类，也可以用于回归，泛化错误率低，计算开销不大，结果容易解释；

2.可以解决小样本情况下的机器学习问题，可以解决高维问题 可以避免神经网络结构选择和局部极小点问题。

3.SVM是最好的现成的分类器，现成是指不加修改可直接使用。并且能够得到较低的错误率，SVM可以对训练集之外的数据点做很好的分类决策。

4.SVM尽量保持与样本间距离的性质导致它抗攻击的能力更强。 引用https://www.cnblogs.com/nolonely/p/6678256.html

这个模型的缺点是什么？什么条件下它表现很差？

回答：对参数调节和和核函数的选择敏感，对缺失数据敏感，原始分类器不加修改仅适用于处理二分类问题。

根据我们当前数据集的特点，为什么这个模型适合这个问题。

回答：这是个分类问题，SVM基本对于分类问题都有较好的适应性。

练习 - 创建一个训练和预测的流水线

为了正确评估你选择的每一个模型的性能，创建一个能够帮助你快速有效地使用不同大小的训练集并在验证集上做预测的训练和验证的流水线是十分重要的。 你在这里实现的功能将会在接下来的部分中被用到。在下面的代码单元中，你将实现以下功能：

• 从sklearn.metrics中导入fbeta_score和accuracy_score。
• 用训练集拟合学习器，并记录训练时间。
• 对训练集的前300个数据点和验证集进行预测并记录预测时间。
• 计算预测训练集的前300个数据点的准确率和F-score。
• 计算预测验证集的准确率和F-score。

In [47]:

# TODO：从sklearn中导入两个评价指标 - fbeta_score和accuracy_scorefrom sklearn.metrics import fbeta_score, accuracy_scoredef train_predict(learner, sample_size, X_train, y_train, X_val, y_val):     '''    inputs:       - learner: the learning algorithm to be trained and predicted on       - sample_size: the size of samples (number) to be drawn from training set       - X_train: features training set       - y_train: income training set       - X_val: features validation set       - y_val: income validation set    '''        results = {}        # TODO：使用sample_size大小的训练数据来拟合学习器    # TODO: Fit the learner to the training data using slicing with 'sample_size'    start = time() # 获得程序开始时间    learner = learner.fit(X_train[:sample_size],y_train[:sample_size])    end = time() # 获得程序结束时间        # TODO：计算训练时间    results['train_time'] = end - start        # TODO: 得到在验证集上的预测值    #       然后得到对前300个训练数据的预测结果    start = time() # 获得程序开始时间    predictions_val = learner.predict(X_val)    predictions_train = learner.predict(X_train[:300])    end = time() # 获得程序结束时间        # TODO：计算预测用时    results['pred_time'] = start-end                # TODO：计算在最前面的300个训练数据的准确率    results['acc_train'] = accuracy_score(y_train[:300],predictions_train)            # TODO：计算在验证上的准确率    results['acc_val'] = accuracy_score(y_val,predictions_val)        # TODO：计算在最前面300个训练数据上的F-score    results['f_train'] =  fbeta_score(y_train[:300], predictions_train, average='macro', beta=0.5)            # TODO：计算验证集上的F-score    results['f_val'] = fbeta_score(y_val, predictions_val, average='macro', beta=0.5)           # 成功    print "{} trained on {} samples.".format(learner.__class__.__name__, sample_size)            # 返回结果    return results

练习：初始模型的评估

在下面的代码单元中，您将需要实现以下功能：

• 导入你在前面讨论的三个监督学习模型。
• 初始化三个模型并存储在'clf_A'，'clf_B'和'clf_C'中。
  • 使用模型的默认参数值，在接下来的部分中你将需要对某一个模型的参数进行调整。
  • 设置random_state (如果有这个参数)。
• 计算1%， 10%， 100%的训练数据分别对应多少个数据点，并将这些值存储在'samples_1', 'samples_10', 'samples_100'中

注意：取决于你选择的算法，下面实现的代码可能需要一些时间来运行！

In [74]:

# TODO：从sklearn中导入三个监督学习模型from sklearn.svm import SVCfrom sklearn.linear_model import LogisticRegressionfrom sklearn.ensemble import RandomForestClassifier# TODO：初始化三个模型clf_A = SVC()clf_B = LogisticRegression()clf_C = RandomForestClassifier(max_depth=2, random_state=0)# TODO：计算1%， 10%， 100%的训练数据分别对应多少点samples_1 = int(X_train.shape[0]*0.01)samples_10 = int(X_train.shape[0]*0.1)samples_100 = int(X_train.shape[0])print [samples_1, samples_10, samples_100]# 收集学习器的结果results = {}for clf in [clf_A, clf_B, clf_C]:    clf_name = clf.__class__.__name__    results[clf_name] = {}    for i, samples in enumerate([samples_1, samples_10, samples_100]):        results[clf_name][i] = train_predict(clf, samples, X_train, y_train, X_val, y_val)        print results# 对选择的三个模型得到的评价结果进行可视化vs.evaluate(results, accuracy, fscore)

[289, 2894, 28941]SVC trained on 289 samples.{'SVC': {0: {'pred_time': -0.11800003051757812, 'train_time': 0.009000062942504883, 'acc_train': 0.79000000000000004, 'f_train': 0.41231732776617958, 'acc_val': 0.75221116639027086, 'f_val': 0.39571640445517198}}}SVC trained on 2894 samples.{'SVC': {0: {'pred_time': -0.11800003051757812, 'train_time': 0.009000062942504883, 'acc_train': 0.79000000000000004, 'f_train': 0.41231732776617958, 'acc_val': 0.75221116639027086, 'f_val': 0.39571640445517198}, 1: {'pred_time': -1.0699999332427979, 'train_time': 0.4760000705718994, 'acc_train': 0.87333333333333329, 'f_train': 0.81155406268108476, 'acc_val': 0.82476506357103374, 'f_val': 0.75963091016161122}}}SVC trained on 28941 samples.{'SVC': {0: {'pred_time': -0.11800003051757812, 'train_time': 0.009000062942504883, 'acc_train': 0.79000000000000004, 'f_train': 0.41231732776617958, 'acc_val': 0.75221116639027086, 'f_val': 0.39571640445517198}, 1: {'pred_time': -1.0699999332427979, 'train_time': 0.4760000705718994, 'acc_train': 0.87333333333333329, 'f_train': 0.81155406268108476, 'acc_val': 0.82476506357103374, 'f_val': 0.75963091016161122}, 2: {'pred_time': -9.663000106811523, 'train_time': 61.973999977111816, 'acc_train': 0.87, 'f_train': 0.80793593339969227, 'acc_val': 0.83139856274184631, 'f_val': 0.76991865965597217}}}LogisticRegression trained on 289 samples.{'LogisticRegression': {0: {'pred_time': -0.003999948501586914, 'train_time': 0.002000093460083008, 'acc_train': 0.88, 'f_train': 0.82137291888788655, 'acc_val': 0.80279159756771701, 'f_val': 0.72676679162997115}}, 'SVC': {0: {'pred_time': -0.11800003051757812, 'train_time': 0.009000062942504883, 'acc_train': 0.79000000000000004, 'f_train': 0.41231732776617958, 'acc_val': 0.75221116639027086, 'f_val': 0.39571640445517198}, 1: {'pred_time': -1.0699999332427979, 'train_time': 0.4760000705718994, 'acc_train': 0.87333333333333329, 'f_train': 0.81155406268108476, 'acc_val': 0.82476506357103374, 'f_val': 0.75963091016161122}, 2: {'pred_time': -9.663000106811523, 'train_time': 61.973999977111816, 'acc_train': 0.87, 'f_train': 0.80793593339969227, 'acc_val': 0.83139856274184631, 'f_val': 0.76991865965597217}}}LogisticRegression trained on 2894 samples.{'LogisticRegression': {0: {'pred_time': -0.003999948501586914, 'train_time': 0.002000093460083008, 'acc_train': 0.88, 'f_train': 0.82137291888788655, 'acc_val': 0.80279159756771701, 'f_val': 0.72676679162997115}, 1: {'pred_time': -0.003999948501586914, 'train_time': 0.010999917984008789, 'acc_train': 0.8666666666666667, 'f_train': 0.79927713446673931, 'acc_val': 0.83886124930901051, 'f_val': 0.78228453933007414}}, 'SVC': {0: {'pred_time': -0.11800003051757812, 'train_time': 0.009000062942504883, 'acc_train': 0.79000000000000004, 'f_train': 0.41231732776617958, 'acc_val': 0.75221116639027086, 'f_val': 0.39571640445517198}, 1: {'pred_time': -1.0699999332427979, 'train_time': 0.4760000705718994, 'acc_train': 0.87333333333333329, 'f_train': 0.81155406268108476, 'acc_val': 0.82476506357103374, 'f_val': 0.75963091016161122}, 2: {'pred_time': -9.663000106811523, 'train_time': 61.973999977111816, 'acc_train': 0.87, 'f_train': 0.80793593339969227, 'acc_val': 0.83139856274184631, 'f_val': 0.76991865965597217}}}LogisticRegression trained on 28941 samples.{'LogisticRegression': {0: {'pred_time': -0.003999948501586914, 'train_time': 0.002000093460083008, 'acc_train': 0.88, 'f_train': 0.82137291888788655, 'acc_val': 0.80279159756771701, 'f_val': 0.72676679162997115}, 1: {'pred_time': -0.003999948501586914, 'train_time': 0.010999917984008789, 'acc_train': 0.8666666666666667, 'f_train': 0.79927713446673931, 'acc_val': 0.83886124930901051, 'f_val': 0.78228453933007414}, 2: {'pred_time': -0.004999876022338867, 'train_time': 0.2390000820159912, 'acc_train': 0.87, 'f_train': 0.80631543397500849, 'acc_val': 0.85364842454394696, 'f_val': 0.80412873644626215}}, 'SVC': {0: {'pred_time': -0.11800003051757812, 'train_time': 0.009000062942504883, 'acc_train': 0.79000000000000004, 'f_train': 0.41231732776617958, 'acc_val': 0.75221116639027086, 'f_val': 0.39571640445517198}, 1: {'pred_time': -1.0699999332427979, 'train_time': 0.4760000705718994, 'acc_train': 0.87333333333333329, 'f_train': 0.81155406268108476, 'acc_val': 0.82476506357103374, 'f_val': 0.75963091016161122}, 2: {'pred_time': -9.663000106811523, 'train_time': 61.973999977111816, 'acc_train': 0.87, 'f_train': 0.80793593339969227, 'acc_val': 0.83139856274184631, 'f_val': 0.76991865965597217}}}RandomForestClassifier trained on 289 samples.{'LogisticRegression': {0: {'pred_time': -0.003999948501586914, 'train_time': 0.002000093460083008, 'acc_train': 0.88, 'f_train': 0.82137291888788655, 'acc_val': 0.80279159756771701, 'f_val': 0.72676679162997115}, 1: {'pred_time': -0.003999948501586914, 'train_time': 0.010999917984008789, 'acc_train': 0.8666666666666667, 'f_train': 0.79927713446673931, 'acc_val': 0.83886124930901051, 'f_val': 0.78228453933007414}, 2: {'pred_time': -0.004999876022338867, 'train_time': 0.2390000820159912, 'acc_train': 0.87, 'f_train': 0.80631543397500849, 'acc_val': 0.85364842454394696, 'f_val': 0.80412873644626215}}, 'SVC': {0: {'pred_time': -0.11800003051757812, 'train_time': 0.009000062942504883, 'acc_train': 0.79000000000000004, 'f_train': 0.41231732776617958, 'acc_val': 0.75221116639027086, 'f_val': 0.39571640445517198}, 1: {'pred_time': -1.0699999332427979, 'train_time': 0.4760000705718994, 'acc_train': 0.87333333333333329, 'f_train': 0.81155406268108476, 'acc_val': 0.82476506357103374, 'f_val': 0.75963091016161122}, 2: {'pred_time': -9.663000106811523, 'train_time': 61.973999977111816, 'acc_train': 0.87, 'f_train': 0.80793593339969227, 'acc_val': 0.83139856274184631, 'f_val': 0.76991865965597217}}, 'RandomForestClassifier': {0: {'pred_time': -0.012000083923339844, 'train_time': 0.02200007438659668, 'acc_train': 0.80000000000000004, 'f_train': 0.51578947368421058, 'acc_val': 0.76257600884466559, 'f_val': 0.48878343687916637}}}RandomForestClassifier trained on 2894 samples.{'LogisticRegression': {0: {'pred_time': -0.003999948501586914, 'train_time': 0.002000093460083008, 'acc_train': 0.88, 'f_train': 0.82137291888788655, 'acc_val': 0.80279159756771701, 'f_val': 0.72676679162997115}, 1: {'pred_time': -0.003999948501586914, 'train_time': 0.010999917984008789, 'acc_train': 0.8666666666666667, 'f_train': 0.79927713446673931, 'acc_val': 0.83886124930901051, 'f_val': 0.78228453933007414}, 2: {'pred_time': -0.004999876022338867, 'train_time': 0.2390000820159912, 'acc_train': 0.87, 'f_train': 0.80631543397500849, 'acc_val': 0.85364842454394696, 'f_val': 0.80412873644626215}}, 'SVC': {0: {'pred_time': -0.11800003051757812, 'train_time': 0.009000062942504883, 'acc_train': 0.79000000000000004, 'f_train': 0.41231732776617958, 'acc_val': 0.75221116639027086, 'f_val': 0.39571640445517198}, 1: {'pred_time': -1.0699999332427979, 'train_time': 0.4760000705718994, 'acc_train': 0.87333333333333329, 'f_train': 0.81155406268108476, 'acc_val': 0.82476506357103374, 'f_val': 0.75963091016161122}, 2: {'pred_time': -9.663000106811523, 'train_time': 61.973999977111816, 'acc_train': 0.87, 'f_train': 0.80793593339969227, 'acc_val': 0.83139856274184631, 'f_val': 0.76991865965597217}}, 'RandomForestClassifier': {0: {'pred_time': -0.012000083923339844, 'train_time': 0.02200007438659668, 'acc_train': 0.80000000000000004, 'f_train': 0.51578947368421058, 'acc_val': 0.76257600884466559, 'f_val': 0.48878343687916637}, 1: {'pred_time': -0.01100015640258789, 'train_time': 0.025999784469604492, 'acc_train': 0.81000000000000005, 'f_train': 0.59173438758327845, 'acc_val': 0.78855721393034828, 'f_val': 0.6405552694090404}}}RandomForestClassifier trained on 28941 samples.{'LogisticRegression': {0: {'pred_time': -0.003999948501586914, 'train_time': 0.002000093460083008, 'acc_train': 0.88, 'f_train': 0.82137291888788655, 'acc_val': 0.80279159756771701, 'f_val': 0.72676679162997115}, 1: {'pred_time': -0.003999948501586914, 'train_time': 0.010999917984008789, 'acc_train': 0.8666666666666667, 'f_train': 0.79927713446673931, 'acc_val': 0.83886124930901051, 'f_val': 0.78228453933007414}, 2: {'pred_time': -0.004999876022338867, 'train_time': 0.2390000820159912, 'acc_train': 0.87, 'f_train': 0.80631543397500849, 'acc_val': 0.85364842454394696, 'f_val': 0.80412873644626215}}, 'SVC': {0: {'pred_time': -0.11800003051757812, 'train_time': 0.009000062942504883, 'acc_train': 0.79000000000000004, 'f_train': 0.41231732776617958, 'acc_val': 0.75221116639027086, 'f_val': 0.39571640445517198}, 1: {'pred_time': -1.0699999332427979, 'train_time': 0.4760000705718994, 'acc_train': 0.87333333333333329, 'f_train': 0.81155406268108476, 'acc_val': 0.82476506357103374, 'f_val': 0.75963091016161122}, 2: {'pred_time': -9.663000106811523, 'train_time': 61.973999977111816, 'acc_train': 0.87, 'f_train': 0.80793593339969227, 'acc_val': 0.83139856274184631, 'f_val': 0.76991865965597217}}, 'RandomForestClassifier': {0: {'pred_time': -0.012000083923339844, 'train_time': 0.02200007438659668, 'acc_train': 0.80000000000000004, 'f_train': 0.51578947368421058, 'acc_val': 0.76257600884466559, 'f_val': 0.48878343687916637}, 1: {'pred_time': -0.01100015640258789, 'train_time': 0.025999784469604492, 'acc_train': 0.81000000000000005, 'f_train': 0.59173438758327845, 'acc_val': 0.78855721393034828, 'f_val': 0.6405552694090404}, 2: {'pred_time': -0.010999917984008789, 'train_time': 0.08100008964538574, 'acc_train': 0.80333333333333334, 'f_train': 0.54354216588424975, 'acc_val': 0.77363184079601988, 'f_val': 0.56357068062942894}}}

---

提高效果

在这最后一节中，您将从三个有监督的学习模型中选择 最好的 模型来使用学生数据。你将在整个训练集（X_train和y_train）上使用网格搜索优化至少调节一个参数以获得一个比没有调节之前更好的 F-score。

问题 3 - 选择最佳的模型

基于你前面做的评价，用一到两段话向 CharityML 解释这三个模型中哪一个对于判断被调查者的年收入大于 $50,000 是最合适的。
提示：你的答案应该包括评价指标，预测/训练时间，以及该算法是否适合这里的数据。

回答：逻辑回归logisticRegression模型最为合适，因为训练时间和预测时间最短，无论在小样本还是全部训练样本上训练的结果都达到了最高分数，远超随机森林，在极个别上与SVC接近。

问题 4 - 用通俗的话解释模型

用一到两段话，向 CharityML 用外行也听得懂的话来解释最终模型是如何工作的。你需要解释所选模型的主要特点。例如，这个模型是怎样被训练的，它又是如何做出预测的。避免使用高级的数学或技术术语，不要使用公式或特定的算法名词。

回答： 模型通过分析数据的各个特征来预测一个公民是否是潜在的捐赠者，对于结果而言要么是要么不是，符合二值分布的逻辑。模型通过多个样本的迭代，不断试错逐渐修正自身，减小预测误差，达到一个较高的精度，通过公民的相关数据特征，做出是否是潜在捐赠者的判断。

练习：模型调优

调节选择的模型的参数。使用网格搜索（GridSearchCV）来至少调整模型的重要参数（至少调整一个），这个参数至少需尝试3个不同的值。你要使用整个训练集来完成这个过程。在接下来的代码单元中，你需要实现以下功能：

• 导入sklearn.model_selection.GridSearchCV 和 sklearn.metrics.make_scorer.
• 初始化你选择的分类器，并将其存储在clf中。
  • 设置random_state (如果有这个参数)。
• 创建一个对于这个模型你希望调整参数的字典。
  • 例如: parameters = {'parameter' : [list of values]}。
  • 注意： 如果你的学习器有 max_features 参数，请不要调节它！
• 使用make_scorer来创建一个fbeta_score评分对象（设置β=0.5β=0.5）。
• 在分类器clf上用'scorer'作为评价函数运行网格搜索，并将结果存储在grid_obj中。
• 用训练集（X_train, y_train）训练grid search object,并将结果存储在grid_fit中。

注意： 取决于你选择的参数列表，下面实现的代码可能需要花一些时间运行！

In [80]:

# TODO：导入'GridSearchCV', 'make_scorer'和其他一些需要的库from sklearn.model_selection import GridSearchCVfrom sklearn.metrics import make_scorer# TODO：初始化分类器clf = LogisticRegression(random_state = 10)# TODO：创建你希望调节的参数列表parameters = {'C':[0.05,1.0,5.5,100.0]}# TODO：创建一个fbeta_score打分对象scorer = make_scorer(fbeta_score, beta=0.5)# TODO：在分类器上使用网格搜索，使用'scorer'作为评价函数grid_obj = GridSearchCV(clf, parameters,scoring=scorer)# TODO：用训练数据拟合网格搜索对象并找到最佳参数grid_obj.fit(X_train,y_train)# 得到estimatorbest_clf = grid_obj.best_estimator_# 使用没有调优的模型做预测predictions = (clf.fit(X_train, y_train)).predict(X_val)best_predictions = best_clf.predict(X_val)# 汇报调参前和调参后的分数print "Unoptimized model\n------"print "Accuracy score on validation data: {:.4f}".format(accuracy_score(y_val, predictions))print "F-score on validation data: {:.4f}".format(fbeta_score(y_val, predictions, beta = 0.5))print "\nOptimized Model\n------"print "Final accuracy score on the validation data: {:.4f}".format(accuracy_score(y_val, best_predictions))print "Final F-score on the validation data: {:.4f}".format(fbeta_score(y_val, best_predictions, beta = 0.5))

Unoptimized model------Accuracy score on validation data: 0.8536F-score on validation data: 0.7182Optimized Model------Final accuracy score on the validation data: 0.8545Final F-score on the validation data: 0.7189

问题 5 - 最终模型评估

你的最优模型在测试数据上的准确率和 F-score 是多少？这些分数比没有优化的模型好还是差？你优化的结果相比于你在问题 1中得到的天真预测器怎么样？
注意：请在下面的表格中填写你的结果，然后在答案框中提供讨论。

结果:

评价指标天真预测器未优化的模型优化的模型准确率0.24780.8536 0.8545F-score0.29170.71820.7189

回答：分数比未优化的模型要好，远超出天真预测器的评价分数。但是提高幅度不大，有可能是选择的参数不够灵敏。

---

特征的重要性

在数据上（比如我们这里使用的人口普查的数据）使用监督学习算法的一个重要的任务是决定哪些特征能够提供最强的预测能力。专注于少量的有效特征和标签之间的关系，我们能够更加简单地理解这些现象，这在很多情况下都是十分有用的。在这个项目的情境下这表示我们希望选择一小部分特征，这些特征能够在预测被调查者是否年收入大于$50,000这个问题上有很强的预测能力。

选择一个有 'feature_importance_' 属性的scikit学习分类器（例如 AdaBoost，随机森林）。'feature_importance_' 属性是对特征的重要性排序的函数。在下一个代码单元中用这个分类器拟合训练集数据并使用这个属性来决定人口普查数据中最重要的5个特征。

问题 6 - 观察特征相关性

当探索数据的时候，它显示在这个人口普查数据集中每一条记录我们有十三个可用的特征。
在这十三个记录中，你认为哪五个特征对于预测是最重要的，选择每个特征的理由是什么？你会怎样对他们排序？

回答：

• 特征1:occupation 职业往往决定一个人考虑问题的角度和整体人际圈的素质，因此这个可能是首要特征。
• 特征2:education_level 受教育水平影响这个人的世界观和个人素养，应该是次要特征；
• 特征3:age 年龄将决定一个人的经历和经验，总体来说年龄越大，在同一行业收入更高的可能性越大。
• 特征4:workclass 职业决定了收入来源。
• 特征5:capital-gain 资本收益直接影响一个人的收入水平。

练习 - 提取特征重要性

选择一个scikit-learn中有feature_importance_属性的监督学习分类器，这个属性是一个在做预测的时候根据所选择的算法来对特征重要性进行排序的功能。

在下面的代码单元中，你将要实现以下功能：

• 如果这个模型和你前面使用的三个模型不一样的话从sklearn中导入一个监督学习模型。
• 在整个训练集上训练一个监督学习模型。
• 使用模型中的 'feature_importances_'提取特征的重要性。

In [76]:

# TODO：导入一个有'feature_importances_'的监督学习模型# TODO：在训练集上训练一个监督学习模型model = RandomForestClassifier(max_depth=2, random_state=10)model.fit(X_train,y_train)# TODO： 提取特征重要性importances = model.feature_importances_# 绘图vs.feature_plot(importances, X_train, y_train)

问题 7 - 提取特征重要性

观察上面创建的展示五个用于预测被调查者年收入是否大于$50,000最相关的特征的可视化图像。

这五个特征的权重加起来是否超过了0.5?
这五个特征和你在问题 6中讨论的特征比较怎么样？
如果说你的答案和这里的相近，那么这个可视化怎样佐证了你的想法？
如果你的选择不相近，那么为什么你觉得这些特征更加相关？

回答：这5个权重加起来超过了0.5，与我在之前回答的特征并不一致。我原以为婚姻对收入影响不大，结果实际来看有无婚姻对收入影响的权重很高。也许是在中国的国情不同，家庭收入并不依赖于配偶的收入水平。

特征选择

如果我们只是用可用特征的一个子集的话模型表现会怎么样？通过使用更少的特征来训练，在评价指标的角度来看我们的期望是训练和预测的时间会更少。从上面的可视化来看，我们可以看到前五个最重要的特征贡献了数据中所有特征中超过一半的重要性。这提示我们可以尝试去减小特征空间，简化模型需要学习的信息。下面代码单元将使用你前面发现的优化模型，并只使用五个最重要的特征在相同的训练集上训练模型。

In [77]:

# 导入克隆模型的功能from sklearn.base import clone# 减小特征空间X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]X_val_reduced = X_val[X_val.columns.values[(np.argsort(importances)[::-1])[:5]]]# 在前面的网格搜索的基础上训练一个“最好的”模型clf_on_reduced = (clone(best_clf)).fit(X_train_reduced, y_train)# 做一个新的预测reduced_predictions = clf_on_reduced.predict(X_val_reduced)# 对于每一个版本的数据汇报最终模型的分数print "Final Model trained on full data\n------"print "Accuracy on validation data: {:.4f}".format(accuracy_score(y_val, best_predictions))print "F-score on validation data: {:.4f}".format(fbeta_score(y_val, best_predictions, beta = 0.5))print "\nFinal Model trained on reduced data\n------"print "Accuracy on validation data: {:.4f}".format(accuracy_score(y_val, reduced_predictions))print "F-score on validation data: {:.4f}".format(fbeta_score(y_val, reduced_predictions, beta = 0.5))

Final Model trained on full data------Accuracy on validation data: 0.8545F-score on validation data: 0.7189Final Model trained on reduced data------Accuracy on validation data: 0.8208F-score on validation data: 0.6418

问题 8 - 特征选择的影响

最终模型在只是用五个特征的数据上和使用所有的特征数据上的 F-score 和准确率相比怎么样？
如果训练时间是一个要考虑的因素，你会考虑使用部分特征的数据作为你的训练集吗？

回答：准确率有所降低，但降低不明显，能够反映基本规律。如果时间是一个考虑因素，我会使用部分特征数据作为训练集。

问题 9 - 在测试集上测试你的模型

终于到了测试的时候，记住，测试集只能用一次。

使用你最有信心的模型，在测试集上测试，计算出准确率和 F-score。 简述你选择这个模型的原因，并分析测试结果

In [81]:

#TODO test your model on testing data and report accuracy and F scorey_test_pred = grid_obj.predict(X_test)print accuracy_score(y_test, y_test_pred)print fbeta_score(y_test, y_test_pred, average='macro', beta=0.5)

0.8485351022660.796557145056

In [82]:

##逻辑回归模型是处理二分类问题首先应该尝试的模型，恰好，这些数据是线性可分的，模型在数据上表现出了较高的性能和较好的准确率。经过网格搜索，模型##选择了最佳参数，达到了理想准确率。

注意： 当你写完了所有的代码，并且回答了所有的问题。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏，这样导出File -> Download as -> HTML (.html)把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。