Hey there! Let's unpack your questions about LIME clearly:

What do min and max in LIME outputs mean? #

When LIME generates an explanation, it first creates a bunch of perturbed versions of your input data (think: slight variations of the original sample, flipping features or adjusting values within plausible ranges). It then runs your target model on all these perturbed samples to get their predicted values.

The min and max you see are simply the minimum and maximum predicted values from all those perturbed samples. They give you a sense of how much the model's predictions can swing when features are adjusted in the range LIME uses for its explanation—super useful for contextualizing how impactful a feature's change really is.

For example: If you're explaining a house price prediction, LIME might generate 500 perturbed house samples, and the model predicts prices from $120k to $350k. Those two numbers would be your min and max in the output.

Is RidgeRegressor LIME's default explanation model? #

Yep, for tabular (table-based) data in the Python LIME package, the default surrogate (interpretable stand-in) model is sklearn.linear_model.RidgeRegressor. That said, defaults vary by data type:

• For text data, LIME uses a logistic regression model with L1 regularization (for classification tasks)
• For image data, it defaults to a linear model with L2 regularization

If you want to swap in a different model, you can pass it via the model_regressor parameter when initializing the explainer. Here's a quick example:

from sklearn.linear_model import LinearRegression
from lime.lime_tabular import LimeTabularExplainer

# Initialize explainer with a custom linear regression model
explainer = LimeTabularExplainer(
    training_data=your_training_data,
    feature_names=your_feature_names,
    model_regressor=LinearRegression()
)

内容的提问来源于stack exchange，提问作者Allyg