Common Attributes and Methods
As model complexity grows, it becomes harder and harder to look inside and understand a model’s inner workings (especially with artificial neural networks). Thankfully, scikit-learn models share several key attributes and methods that provide valuable insight into how a model has learned from data. For instance, attributes like coef_ and intercept_ in linear models store the learned coefficients and intercepts, helping interpret model behavior.
Similarly, methods such as score() allow users to evaluate model performance, typically returning a default metric like accuracy for classifiers or R² for regressors. These common features ensure consistency across different models and simplify model analysis and interpretation.
from sklearn.linear_model import LinearRegression
import numpy as np
# Example data
X = np.array([[1], [2], [3], [4], [5]]) # Feature matrix
y = np.array([1, 2, 3, 3.5, 5]) # Target values
# Create and fit the model
model = LinearRegression...
