Which method can be used to get the names of the features seen or used by the linear regression algorithm while fitting the dataset?

The method to get the names of the features seen or used by the linear regression algorithm while fitting the dataset depends on the programming language and the specific library you are using. However, in Python using scikit-learn library, there isn't a direct method to get feature names after fitting a linear regression model because the model itself does not store this information.

Here are the steps you can follow to keep track of feature names:

1. Load your dataset into a pandas DataFrame. Pandas DataFrames are a way to store data in grid-like format. This can be helpful because you can name each of the columns in the DataFrame.

import pandas as pd
data = pd.read_csv('your_data.csv')

2. When you define your features and target for the linear regression model, make sure to use the column names. This way, you are explicitly defining what features are being used.

features = data[['feature1', 'feature2', 'feature3']]
target = data['target']

3. Fit your linear regression model with your features and target.

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(features, target)

4. Since you've used a DataFrame to store your data and you've explicitly defined your features, you can get the feature names from the DataFrame.

feature_names = features.columns

In this way, feature_names will hold the names of the features used by the linear regression model.