Chapter 10
第三章 模型搭建和评估-评估
NotebookPython 348 cells
第三章 模型搭建和评估-评估
根据之前的模型的建模，我们知道如何运用sklearn这个库来完成建模，以及我们知道了的数据集的划分等等操作。那么一个模型我们怎么知道它好不好用呢？以至于我们能不能放心的使用模型给我的结果呢？那么今天的学习的评估，就会很有帮助。
加载下面的库
In [1]python · cell 4
python
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.display import Image
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifierIn [2]python · cell 5
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%matplotlib inlineIn [3]python · cell 6
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['figure.figsize'] = (10, 6) # 设置输出图片大小任务：加载数据并分割测试集和训练集
In [5]python · cell 8
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from sklearn.model_selection import train_test_splitIn [6]python · cell 9
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# 一般先取出X和y后再切割，有些情况会使用到未切割的，这时候X和y就可以用,x是清洗好的数据，y是我们要预测的存活数据'Survived'
data = pd.read_csv('clear_data.csv')
train = pd.read_csv('train.csv')
X = data
y = train['Survived']In [7]python · cell 10
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# 对数据集进行切割
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)In [8]python · cell 11
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# 默认参数逻辑回归模型
lr = LogisticRegression()
lr.fit(X_train, y_train)Output
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)模型评估
- 模型评估是为了知道模型的泛化能力。
- 交叉验证（cross-validation）是一种评估泛化性能的统计学方法，它比单次划分训练集和测试集的方法更加稳定、全面。
- 在交叉验证中，数据被多次划分，并且需要训练多个模型。
- 最常用的交叉验证是 k 折交叉验证（k-fold cross-validation），其中 k 是由用户指定的数字，通常取 5 或 10。
- 准确率（precision）度量的是被预测为正例的样本中有多少是真正的正例
- 召回率（recall）度量的是正类样本中有多少被预测为正类
- f-分数是准确率与召回率的调和平均
【思考】：将上面的概念进一步的理解，大家可以做一下总结
In [9]python · cell 15
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#思考回答：
任务一：交叉验证
- 用10折交叉验证来评估之前的逻辑回归模型
- 计算交叉验证精度的平均值
In [13]python · cell 17
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#提示：交叉验证
Image('Snipaste_2020-01-05_16-37-56.png')Output
<IPython.core.display.Image object>
提示4
- 交叉验证在sklearn中的模块为
sklearn.model_selection
In [14]python · cell 19
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from sklearn.model_selection import cross_val_scoreIn [15]python · cell 20
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lr = LogisticRegression(C=100)
scores = cross_val_score(lr, X_train, y_train, cv=10)Output
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
In [16]python · cell 21
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# k折交叉验证分数
scoresOutput
array([0.85074627, 0.74626866, 0.74626866, 0.80597015, 0.88059701,
0.8358209 , 0.76119403, 0.8358209 , 0.74242424, 0.75757576])In [17]python · cell 22
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# 平均交叉验证分数
print("Average cross-validation score: {:.2f}".format(scores.mean()))Output
Average cross-validation score: 0.80
思考4
- k折越多的情况下会带来什么样的影响？
In [18]python · cell 24
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#思考回答
任务二：混淆矩阵
- 计算二分类问题的混淆矩阵
- 计算精确率、召回率以及f-分数
【思考】什么是二分类问题的混淆矩阵，理解这个概念，知道它主要是运算到什么任务中的
In [19]python · cell 27
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#思考回答
In [20]python · cell 28
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#提示：混淆矩阵
Image('Snipaste_2020-01-05_16-38-26.png')Output
<IPython.core.display.Image object>
In [21]python · cell 29
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#提示：准确率 (Accuracy),精确度（Precision）,Recall,f-分数计算方法
Image('Snipaste_2020-01-05_16-39-27.png')Output
<IPython.core.display.Image object>
提示5
- 混淆矩阵的方法在sklearn中的
sklearn.metrics模块 - 混淆矩阵需要输入真实标签和预测标签
- 精确率、召回率以及f-分数可使用
classification_report模块
In [22]python · cell 31
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from sklearn.metrics import confusion_matrixIn [23]python · cell 32
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# 训练模型
lr = LogisticRegression(C=100)
lr.fit(X_train, y_train)Output
/Users/chenandong/anaconda3/envs/tensorflow1.15/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
LogisticRegression(C=100, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)In [24]python · cell 33
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# 模型预测结果
pred = lr.predict(X_train)In [25]python · cell 34
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# 混淆矩阵
confusion_matrix(y_train, pred)Output
array([[354, 58],
[ 83, 173]])In [26]python · cell 35
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from sklearn.metrics import classification_reportIn [27]python · cell 36
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# 精确率、召回率以及f1-score
print(classification_report(y_train, pred))Output
precision recall f1-score support
0 0.81 0.86 0.83 412
1 0.75 0.68 0.71 256
accuracy 0.79 668
macro avg 0.78 0.77 0.77 668
weighted avg 0.79 0.79 0.79 668
【思考】
- 如果自己实现混淆矩阵的时候该注意什么问题
In [28]python · cell 38
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#思考回答
任务三：ROC曲线
- 绘制ROC曲线
【思考】什么是ROC曲线，OCR曲线的存在是为了解决什么问题？
In [29]python · cell 41
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#思考
提示6
- ROC曲线在sklearn中的模块为
sklearn.metrics - ROC曲线下面所包围的面积越大越好
In [30]python · cell 43
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from sklearn.metrics import roc_curveIn [31]python · cell 44
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fpr, tpr, thresholds = roc_curve(y_test, lr.decision_function(X_test))
plt.plot(fpr, tpr, label="ROC Curve")
plt.xlabel("FPR")
plt.ylabel("TPR (recall)")
# 找到最接近于0的阈值
close_zero = np.argmin(np.abs(thresholds))
plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10, label="threshold zero", fillstyle="none", c='k', mew=2)
plt.legend(loc=4)Output
<matplotlib.legend.Legend at 0x1a1c4b9b38>
findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.
<Figure size 720x432 with 1 Axes>
思考6
- 对于多分类问题如何绘制ROC曲线
In [45]python · cell 46
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#思考回答
【思考】你能从这条OCR曲线的到什么信息？这些信息可以做什么？
In [ ]python · cell 48
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#思考回答