机器学习笔记08：模型的保存与逻辑回归

文章目录

• 机器学习笔记08：模型的保存与逻辑回归
• • 1.模型的保存与加载
  • 2.逻辑回归：分类算法

1.模型的保存与加载

1. API：from sklearn.externals import joblib

joblib.dump(lr,"目录xxxxx.pkl")

# 使用
model = joblib.load("目录xxxxx.pkl")
y_predict = std_y.inverse_transform(model.predict(x_test))

2.逻辑回归：分类算法

1. 回归的输入——>sigmoid——>概率值
2. 损失函数：
3. 梯度下降局部最优的问题：
   1. 多次随机初始化
   2. 求解过程中，调整学习率
4. API：
   sklearn.linear_model.LogisticRegression(penalty=‘l2’, C = 1.0)
   自带正则化 只能解决二分类问题
   Logistic回归分类器
   coef_：回归系数
5. 哪一个类别的样本数量少，判定概率值的值就针对这个类别。

# 逻辑回归
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression,SGDRegressor,LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error,classification_report
import pandas as pd
import numpy as np

def logistic():
    
    # 构造列标签
    column = ['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("breast-cancer-wisconsin.data",names = column)
    
    # 缺失值处理
    data = data.replace(to_replace = '?',value = np.nan)
    data = data.dropna()
    
    # 进行数据的分割
    
    x_train,x_test,y_train,y_test = train_test_split(data[column[1:10]],data[column[10]],test_size = 0.25)
    
    # 进行标准化处理
    std = StandardScaler()
    
    
    # 对特征值进行标准化
    
    x_train = std.fit_transform(x_train)
    x_test = std.transform(x_test)
    
    # 逻辑回归
    lg = LogisticRegression(C = 1.0)
    lg.fit(x_train,y_train)
    print(lg.coef_)
    print("准确率：",lg.score(x_test,y_test))

    # 召回率
    y_predict = lg.predict(x_test)
    print("召回率",classification_report(y_test,y_predict,labels = [2,4],target_names =['良性','恶性']) )
    
    return None

if __name__ == "__main__":
    logistic()

1. 多分类问题：在神经网络内
2. 判别模型和生成模型

本文地址：https://blog.csdn.net/fafagege11520/article/details/109627200