# Logistic Regression - Code

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.

If you start to learn Logistic Regression recently, I recommend you to read Logistic Regression first. The following code is partially inspired from 【机器学习】求解逻辑回归参数（三种方法代码实现）

## First Code

```
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))
```

## Second code

```
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。

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