In machine learning, the calculation of feature weights is an important step in understanding how a model makes predictions. By calculating feature weights, we can understand which features contribute the most to the prediction results of the model, thereby optimizing feature selection and model performance. This article will introduce in detail how to calculate feature weights based on a given model using Python, including linear regression, feature selection methods, and actual cases.
1. The importance of feature weights
Feature weight calculation is an important part of the field of machine learning. It can help us understand the extent to which different features affect the model, thereby optimizing model selection and feature engineering. By feature weights, we can:
Optimized feature selection: Select the feature that contributes the most to the model prediction results, reduce redundant features, and improve model performance.
Understanding the model: Understand which features have a significant impact on the model's prediction results, thereby explaining the model's prediction results.
Improve the model: Adjust feature engineering strategies according to feature weights, such as feature scaling, feature transformation, etc., to further improve model performance.
2. Calculation of feature weights in linear regression
Linear regression is a model used to solve the regression problem, predicting a continuous value output through multiple features. The weights of the model reflect the contribution of each feature to the predicted value.
1. Import the necessary libraries
First, we need to import some libraries for data processing and model training.
import numpy as np import pandas as pd import as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression
2. Create a sample dataset
Suppose we want to predict housing prices, and the characteristics include the number of rooms, area and location.
# Create sample datadata = {
'Number of rooms': [1, 2, 3, 4, 5],
'area': [40, 60, 80, 100, 120],
'Location': [1, 2, 3, 1, 2], # 1: Downtown, 2: Suburbs, 3: Countryside 'House Price': [100, 150, 200, 250, 300]
}
df = (data)
# Features and tagsX = df[['Number of rooms', 'area', 'Location']]
y = df['House Price']
3. Split the dataset
Before model training, we need to segment the dataset into training sets and test sets to evaluate the performance of the model.
# Split datasetX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
4. Train the linear regression model and calculate the weight
Use the fit() method to train the model and use the coef_ attribute to get the weight of the feature.
# Create a linear regression modelmodel = LinearRegression()
# Train the model(X_train, y_train)
# Get weightweights = model.coef_
intercept = model.intercept_
# Visualize weightsfeatures =
(features, weights)
('Weight')
('feature')
('Feature weight visualization')
(0, color='grey', lw=0.8)
()By visualizing the weights, we can clearly see the importance of different characteristics to housing price predictions.
3. Feature selection method
Feature selection is a process of selecting basic features that are more consistent, non-redundant and more relevant to the ML model. Using feature selection in ML projects is necessary because it helps reduce the size and complexity of the dataset, avoid overfitting, and use less time to train the model and perform inference.
1. Forward feature selection
Fit the model using a feature (or a small portion) and constantly add the features until the newly added model has no effect on the ML model metrics. Methods such as correlation analysis (for example, based on Pearson coefficients) can be used.
2. Backward feature selection
In contrast to forward feature selection, start with the complete feature set and then iteratively reduce the features one by one, as long as the ML model metrics remain unchanged.
3. Filtered-based method
This approach is the most direct, with the choice of feature independent of any machine learning algorithm. Using statistics (such as Pearson correlation coefficient, LDA, etc.), important features are selected based on how each feature affects the target outcome. This is the lowest computationally intensive and fastest method.
4. Wrapper-based method (Wrapper)
This method selects features based on the results of ML training indicators. Each subset gets a score after training, then adds or removes the features and stops when the desired ML metric threshold is finally reached. This method can be forward, backward, or recursive. This is the most computationally intensive method because many ML models need to be trained and judged and selected one by one.
5. Embedded method (Embedded)
This method is more complex, and it combines the above two methods together. The most popular examples of this approach are LASSO and tree algorithms.
4. Actual cases: Financial Technology Dataset
We will use a fintech dataset that contains data from past loan applicants such as credit ratings, applicant income, DTI, and other characteristics. The ultimate goal is to use ML to predict whether a loan applicant may default (the loan cannot be paid).
1. Import the dataset
%matplotlib inline
from matplotlib import pyplot as plt
pd.set_option('display.float_format', lambda x: '%.0f' % x)
loan = pd.read_csv('../input/lending-club/accepted_2007_to_2018Q4.', compression='gzip', low_memory=True)
The dataset contains over 2 million rows (we call them samples) and over 150 features. This is a considerable amount of data, which usually contains a lot of "noise" and does not help our ML work, so we need to verify the quality and applicability of the data before ML training occurs.
2. Feature selection
It can take a lot of computing resources and time to analyze such a detailed feature list. So we need to know the properties of each dataset in detail and consult industry experts on which characteristics are necessary. For example, in the example of a fintech dataset, it may be necessary to consult a loan officer who performs a loan assessment every day. Loaners will know exactly what drives their decision-making process (we actually want to automate this part of the process through ML).
Suppose we have received the following suggestions:
loans = loan[['id', 'loan_amnt', 'term', 'int_rate', 'sub_grade', 'emp_length', 'grade', 'annual_inc', 'loan_status', 'dti', 'mths_since_recent_inq', 'revol_util', 'bc_open_to_buy', 'bc_util', 'num_op_rev_tl']] # Remove missing valuesloans = ()
3. Data processing
The steps include missing values, outliers and classification feature processing.
# Handle outliersq_low = loans["annual_inc"].quantile(0.08) q_hi = loans["annual_inc"].quantile(0.92) loans = loans[(loans["annual_inc"] < q_hi) & (loans["annual_inc"] > q_low)] loans = loans[(loans['dti'] <= 45)]
4. Train the model and calculate the weight
We can use linear regression models to calculate feature weights.
# Features and tagsX = loans[['loan_amnt', 'term', 'int_rate', 'sub_grade', 'emp_length', 'grade', 'annual_inc', 'dti', 'mths_since_recent_inq', 'revol_util', 'bc_open_to_buy', 'bc_util', 'num_op_rev_tl']]
y = loans['loan_status'].apply(lambda x: 1 if x == 'Charged Off' else 0) # Convert loan status to binary tags
# Split datasetX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a linear regression modelmodel = LinearRegression()
(X_train, y_train)
# Get weightweights = model.coef_
# Visualize weightsfeatures =
(features, weights)
('Weight')
('feature')
('Feature weight visualization')
(0, color='grey', lw=0.8)
()By visualizing the weights, we can understand which features have the greatest impact on loan default predictions, optimizing feature selection and model performance.
5. Summary
This article details how to calculate feature weights based on a given model using Python, including linear regression, feature selection methods, and actual cases. Feature weight calculation can help us gain insight into the degree of dependence on features of the model, and thus optimize feature selection and model performance. In practical applications, it is very important to select different models and features to calculate weights according to specific problems.
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