In this article, I'll introduce you to feature selection techniques: filtering, wrapping, and embedding. Feature selection is a very important link in machine learning and data mining. It can help us extract valuable information, reduce computational complexity, and improve model performance. Through this article, you will understand the principles, advantages and disadvantages of these three methods, and demonstrate their effects in practical applications through code examples.
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1. What is feature selection?
Feature selection, also known as attribute selection or variable selection, refers to selecting a subset from the original feature set, which contains the features that have the greatest impact on the target variable. The purpose of feature selection is to reduce dimensionality, reduce noise, and improve the generalization ability and interpretability of the model. There are three main types of feature selection methods: filtering, wrapping, and embedding.
2. Filtration method
The Filter Method is a method of selecting features based on the statistical properties of the features themselves. It makes selections based on the degree of association between the features and the target variable. Commonly used filtering methods are: chi-square test, correlation coefficient, mutual information, etc.
Advantages: Simple calculation and fast speed.
Disadvantage: May ignore the interrelationships between features.
3. Packaging method
The Wrapper Method is a method of selecting features based on the performance of the learner. It regards feature selection as a search problem, through the training and evaluation of the learner to find the optimal feature subset. Commonly used packaging methods are: recursive feature elimination (RFE), forward selection (Forward Selection), backward selection (Backward Selection), etc.
Advantages: Considering the relationship between features, it is possible to find the optimal feature subset.
Disadvantages: high computational complexity, requiring a lot of computing resources and time.
4. Embedding method
The Embedded Method is a method for feature selection during model training. It decides which features are important according to the training process of the learner. Commonly used embedding methods are: LASSO regression, ridge regression, decision tree, etc.
Advantages: Considering the relationship between features, it can find the optimal feature subset, and the computational complexity is relatively low.
Disadvantages: It is related to a specific learner and not universal.
5. Code example
Here is a simple example of filtering, wrapping, and embedding using Python and the scikit-learn library:
import numpy as np
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectKBest, chi2, RFE
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
data = load_iris()
X, y = data.data, data.target
# 过滤法 - 使用卡方检验选择最佳特征
X_new = SelectKBest(chi2, k=2).fit_transform(X, y)
print("Filtered features: \n", X_new[:5])
# 包装法 - 使用递归特征消除
model = LogisticRegression(solver='liblinear', multi_class='ovr')
rfe = RFE(model, n_features_to_select=2)
X_new = rfe.fit_transform(X, y)
print("Wrapped features: \n", X_new[:5])
# 嵌入法 - 使用随机森林进行特征选择
model = RandomForestClassifier()
model.fit(X, y)
importances = model.feature_importances_
indices = np.argsort(importances)[-2:]
X_new = X[:, indices]
print("Embedded features: \n", X_new[:5])
6. Summary
This article details feature selection techniques: filtering, wrapping, and embedding. Through the discussion of the principles and practical applications of these three methods, we understand their importance in feature selection. In actual projects, you can choose the appropriate feature selection method according to the characteristics of the data and the problem.
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