Logistic regression algorithm example in Python

Logistic regression algorithm example in Python

Logistic regression is a commonly used classification algorithm that is widely used in the fields of machine learning and data analysis. This article will introduce how to use the logistic regression algorithm for classification in Python. The specific implementation steps are as follows:

Step 1: Import the necessary Python libraries

Before starting to implement the logistic regression algorithm, we need Import the necessary Python libraries, such as: NumPy and pandas (for data processing and cleaning), sklearn and matplotlib (for model training and evaluation, and data visualization). The specific code is as follows:

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score,confusion_matrix
import matplotlib.pyplot as plt

%matplotlib inline

Step 2: Load the data set

Next, we need to load the data set. Here we take the iris data set (iris) as an example. This data set contains 4 characteristics of 3 different species of iris (Setosa, Versicolour and Virginica): sepal length, sepal width, petal length and petal width. We can load the dataset by using pandas to read the data file in CSV format. The specific code is as follows:

data=pd.read_csv('iris.csv')
print(data.head())

Step 3: Data processing and separation

Before inputting the data into the logistic regression model , we need to process and separate data. First, we need to separate the label column in the dataset as our target variable (y), while using the remaining feature columns as our independent variables (X). Secondly, we need to process the data, including: processing missing data, processing outliers, converting categorical variables, etc. Here, we can randomly split the data set into training data and test data by using the train_test_split function in the sklearn library. The specific code is as follows:

X=data.iloc[:,:-1]
y=data.iloc[:,-1]

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)

Step 4: Model training and evaluation

Now, we can use the logistic regression algorithm to fit the training data. We can create a logistic regression object by using the LogisticRegression class in the sklearn library, and then use the fit method to fit the training data. After training is completed, we can use the predict method to predict the test data, and use the accuracy_score and confusion_matrix functions in the sklearn library to evaluate the model. The specific code is as follows:

lr=LogisticRegression()
lr.fit(X_train,y_train)

y_pred=lr.predict(X_test)

acc=accuracy_score(y_test,y_pred)
cm=confusion_matrix(y_test,y_pred)

print('Accuracy:', acc)
print('Confusion Matrix:
', cm)

Step 5: Visualization of results

Finally, we can use the matplotlib library to visualize our results. For example, we can use scatter plots or histograms to show the characteristics of the data and logistic regression classification boundaries. The specific code is as follows:

colors=['blue','green','red']
markers=['o','s','^']
labels=['Setosa','Versicolour','Virginica']

for i, target in enumerate(set(data.iloc[:,-1])):
    plt.scatter(x=data.loc[data.iloc[:,-1]==target,'petal_length'], 
                y=data.loc[data.iloc[:,-1]==target,'petal_width'],
                c=colors[i],
                marker=markers[i],
                label=labels[i])

x=np.linspace(0,8,1000)
y=(-lr.coef_[0][0]*x-lr.intercept_)/lr.coef_[0][1]
plt.plot(x,y,'k-',label='Decision Boundary')

plt.xlabel('Petal Length')
plt.ylabel('Petal Width')
plt.legend(loc='lower right')
plt.show()

In summary, the above are the basic steps to implement the logistic regression algorithm in Python, which can be adjusted and improved according to specific data sets and classification problems. Although the logistic regression algorithm is simple and easy to use, it also requires a full understanding of its principles and appropriate data processing and model optimization to achieve better classification results.

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