什么是逻辑回归

应用场景

逻辑回归的原理

掌握逻辑回归，必须掌握以下两点

• 逻辑回归中，其输入值是什么
• 如何判断逻辑回归的输出

输入

激活函数

衡量损失

损失

优化

API

肿瘤预测案例

数据介绍

代码实现

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

# 获取数据
names = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape','Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin','Normal Nucleoli', 'Mitoses', 'Class']
data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data",names=names)
data.head()

# 数据基本处理
# 缺失值处理
data = data.replace(to_replace="?",value=np.nan)
data = data.dropna()
# 确定特征值，目标值
x = data.iloc[:,1:-1]
y = data["Class"]
# 分割数据
x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=22,test_size=0.2)
# 特征工程 标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 机器学习
estmator = LogisticRegression()
estmator.fit(x_train,y_train)
# 模型评估
print("准确率:\n",estmator.score(x_test,y_test))
print("预测值:\n",estmator.predict(x_test))

分类评估方法

准确率与召回率

混淆矩阵

我们之前使用的准确率公式为：（TP+TN）/(TP+Fn+FP+TN)

精确率(Precision)与召回率(Recall)

精确率：(TP)/(TP+FP)

召回率：(TP)/(TP+FN)

F1-score

分类评估报告api

from sklearn.metrics import classification_report
y_pre = estmator.predict(x_test)
ret = classification_report(y_test,y_pre,labels=(2,4),target_names=("良性","恶性"))
print(ret)

ROC曲线与AUC指标

TPR与FPR

ROC曲线

AUC指标

AUC计算API

from sklearn.metrics import roc_auc_score
y_test = np.where(y_test>3,1,0)
roc_auc_score(y_test,y_pre)

解决类别不平衡问题

pip3 install imbalanced-learn

准备类别不平衡数据

from sklearn.datasets import make_classification
import matplotlib.pylab as plt
from collections import Counter

X,Y = make_classification(n_samples=5000,
                          n_features=2, # 特征个数= n_informative（）+ n_redundant（）+ n_repeated（）
                          n_informative=2,# 多信息特征的个数
                          n_redundant=0,# 冗余信息，informative特征的随机线性组合
                          n_repeated=0,# 重复信息，随机提取n_informative和n_redundant特征
                          n_classes=3,# 分类类别
                          n_clusters_per_class=1,# 某一个类别是由几个cluster构成的
                          weights=[0.01,0.05,0.94],# 列表类型，权重比
                          random_state=0)

X,Y,X.shape

Counter(y)

# 数据可视化
plt.scatter(X[:,0],X[:,1],c=Y)
plt.show()

解决办法

过采样方法

随机过采样方法

from imblearn.over_sampling import RandomOverSampler
ros = RandomOverSampler(random_state=0)
X_resampled,Y_resampled = ros.fit_resample(X,Y)
Counter(Y_resampled)

# 数据可视化
plt.scatter(X_resampled[:,0],X_resampled[:,1],c=Y_resampled)
plt.show()

过采样代表性算法-SMOTE

from imblearn.over_sampling import SMOTE

X_resampled,Y_resampled = SMOTE().fit_resample(X,Y)
Counter(Y_resampled)

# 数据可视化
plt.scatter(X_resampled[:,0],X_resampled[:,1],c=Y_resampled)
plt.show()

欠采样方法

随机欠采样方法

from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler(random_state=0)
X_resampled,Y_resampled = rus.fit_resample(X,Y)
Counter(Y_resampled)

# 数据可视化
plt.scatter(X_resampled[:,0],X_resampled[:,1],c=Y_resampled)
plt.show()