Machine Learning 简明教程

Machine Learning - Forward Feature Construction

前向特征构建是机器学习中的一种特征选择方法，在此方法中我们从一个空的特征集开始，并迭代添加每个步骤中表现最好的特征，直到达到所需的特征数量为止。

Forward Feature Construction is a feature selection method in machine learning where we start with an empty set of features and iteratively add the best performing feature at each step until the desired number of features is reached.

特征选择的目标是识别与预测目标变量最相关的最重要特征，同时忽略对模型增加噪声并且可能导致过拟合的较不重要的特征。

The goal of feature selection is to identify the most important features that are relevant for predicting the target variable, while ignoring the less important features that add noise to the model and may lead to overfitting.

前向特征构造涉及以下步骤−

The steps involved in Forward Feature Construction are as follows −

  1. Initialize an empty set of features.

  2. Set the maximum number of features to be selected.

  3. Iterate until the desired number of features is reached − For each remaining feature that is not already in the set of selected features, fit a model with the selected features and the current feature, and evaluate its performance using a validation set. Select the feature that leads to the best performance and add it to the set of selected features.

  4. Return the set of selected features as the optimal set for the model.

前向特征构造的主要优势在于它的计算效率高、可用于高维数据集。但是，它可能并不总是导致最优特征集，尤其是在特征之间存在高度相关或非线性关系时。

The key advantage of Forward Feature Construction is that it is computationally efficient and can be used for high-dimensional datasets. However, it may not always lead to the optimal set of features, especially if there are highly correlated features or non-linear relationships between the features and the target variable.

Example

下面是使用 Python 实现前向特征构造的一个示例 −

Here is an example to implement Forward Feature Construction in Python −

# Importing the necessary libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Load the diabetes dataset
diabetes = pd.read_csv(r'C:\Users\Leekha\Desktop\diabetes.csv')

# Define the predictor variables (X) and the target variable (y)
X = diabetes.iloc[:, :-1].values
y = diabetes.iloc[:, -1].values

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Create an empty set of features
selected_features = set()

# Set the maximum number of features to be selected
max_features = 8

# Iterate until the desired number of features is reached
while len(selected_features) < max_features:

   # Set the best feature and the best score to be 0
   best_feature = None
   best_score = 0

   # Iterate over all the remaining features
   for i in range(X_train.shape[1]):

      # Skip the feature if it's already selected
      if i in selected_features:
         continue

      # Select the current feature and fit a linear regression model
      X_train_selected = X_train[:, list(selected_features) + [i]]
      regressor = LinearRegression()
      regressor.fit(X_train_selected, y_train)

      # Compute the score on the testing set
      X_test_selected = X_test[:, list(selected_features) + [i]]
      score = regressor.score(X_test_selected, y_test)

      # Update the best feature and score if the current feature performs better
      if score > best_score:
         best_feature = i
         best_score = score

   # Add the best feature to the set of selected features
   selected_features.add(best_feature)

   # Print the selected features and the score
   print('Selected Features:', list(selected_features))
   print('Score:', best_score)

Output

在执行时，它会产生以下输出 −

On execution, it will produce the following output −

Selected Features: [1]
Score: 0.23530716168783583
Selected Features: [0, 1]
Score: 0.2923143573608237
Selected Features: [0, 1, 5]
Score: 0.3164103491569179
Selected Features: [0, 1, 5, 6]
Score: 0.3287368302427327
Selected Features: [0, 1, 2, 5, 6]
Score: 0.334586804842275
Selected Features: [0, 1, 2, 3, 5, 6]
Score: 0.3356264736550455
Selected Features: [0, 1, 2, 3, 4, 5, 6]
Score: 0.3313166516703744
Selected Features: [0, 1, 2, 3, 4, 5, 6, 7]
Score: 0.32230203252064216