毫升 |逻辑回归与决策树分类

逻辑回归和决策树分类是当今使用的两种最流行和最基本的分类算法。没有一种算法比另一种更好，一个算法的卓越性能通常归功于正在处理的数据的性质。

我们可以在不同的类别上比较这两种算法——

Criteria	Logistic Regression	Decision Tree Classification
Interpretability	Less interpretable	More interpretable
Decision Boundaries	Linear and single decision boundary	Bisects the space into smaller spaces
Ease of Decision Making	A decision threshold has to be set	Automatically handles decision making
Overfitting	Not prone to overfitting	Prone to overfitting
Robustness to noise	Robust to noise	Majorly affected by noise
Scalability	Requires a large enough training set	Can be trained on a small training set

作为一个简单的实验，我们在同一个数据集上运行这两个模型并比较它们的性能。

第 1 步：导入所需的库

Python3

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier

第 2 步：读取和清理数据集

Python3

cd C:\Users\Dev\Desktop\Kaggle\Sinking Titanic
# Changing the working location to the location of the file
df = pd.read_csv('_train.csv')
y = df['Survived']
 
X = df.drop('Survived', axis = 1)
X = X.drop(['Name', 'Ticket', 'Cabin', 'Embarked'], axis = 1)
 
X = X.replace(['male', 'female'], [2, 3])
# Hot-encoding the categorical variables
 
X.fillna(method ='ffill', inplace = True)
# Handling the missing values

第 3 步：训练和评估逻辑回归模型

Python3

X_train, X_test, y_train, y_test = train_test_split(
            X, y, test_size = 0.3, random_state = 0)
 
lr = LogisticRegression()
lr.fit(X_train, y_train)
print(lr.score(X_test, y_test))

第 4 步：训练和评估决策树分类器模型

Python3

criteria = ['gini', 'entropy']
scores = {}
 
for c in criteria:
    dt = DecisionTreeClassifier(criterion = c)
    dt.fit(X_train, y_train)
    test_score = dt.score(X_test, y_test)
    scores = test_score
 
print(scores)

在比较分数时，我们可以看到逻辑回归模型在当前数据集上的表现更好，但情况可能并非总是如此。