I have written logistic regression before. But I only introduce some basic knowledge about it. In this article, I will publish the code of the machine learning algorithm in python. One with scikit-learn library; Other is written based on Gradient descent algorithm.
from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.datasets import load_breast_cancer bc = load_breast_cancer() # Load test data X = bc.data y = bc.target X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2) # Split training and test data lr = LogisticRegression() lr.fit(X_train,y_train) # Train the model print(lr.score(X_test,y_test))
import numpy as np from sklearn.datasets import load_breast_cancer def sigmoid(theta1, x, theta0): # Define sigmoid function z = (theta1 * x + theta0).astype("float_") return 1.0 / (1.0 + np.exp(-z)) # Return a list of the results predicted by sigmoid function class LogisticRegression: theta_1 = 0 # Define the coefficient theta_0 = 0 flag = 0 # Distiguish whether the model was trained def gradientDecline(self, theta1, x, theta0, y): # Define the Gradient Decline method sp = sigmoid(theta1,x,theta0) return 1/len(y) * np.sum((y - sp) * x), 1/len(y) * np.sum(y - sp) def fit(self, x, y,alpha = 0.2,trials = 20): # alpha is the learning rate, trials is the trials theta1 = 0.1 theta0 = 0.2 for i in range(0,20): n1, n0 = self.gradientDecline(theta1,x,theta0,y) theta1 = theta1 - alpha * n1 theta0 = theta0 - alpha * n0 print("a = " + str(theta1)) print("b = " + str(theta0)) self.theta_1 = theta1 self.theta_2 = theta2 self.flag = 1
注：算法的核心思想是梯度下降算法，针对不同的矩阵的数据需要进行适当调整；模型拟合可以通过分数可以通过 F1-score 来衡量，具体可以参考逻辑回归。
Three ways to write a logistic regression in python: https://blog.csdn.net/Daycym/article/details/80627682