How can you know the employed Scikit Learn model after training in Python

When working with data science and machine learning in Python, Scikit Learn is a popular library for building and training models. After training a model using Scikit Learn, it’s important to know which specific model was employed in the training process. This can be achieved through various techniques and methods provided by the library.

Inspecting the trained model

One way to know the employed model after training is by inspecting the attributes and properties of the trained model object. For example, if you have trained a classification model such as a decision tree or logistic regression, you can access the ‘model’ attribute of the trained object to determine the specific model employed in the training process.


# Example code for inspecting the trained model
trained_model = # your trained model object
model_type = type(trained_model).__name__
print("Employed model:", model_type)


Using model evaluation

Another approach to identify the employed model is by using model evaluation techniques. For example, you can use cross-validation or grid search to evaluate the performance of multiple models and select the best performing one. This can give you insights into which model was employed based on its performance metrics.

Using model-specific attributes

Some models have specific attributes or properties that can be used to identify them. For instance, decision trees have feature_importances_ attribute which can be accessed after training to gain insights into the employed model. Similarly, logistic regression models have coefficients that can be examined to determine the model type.

Conclusion

Identifying the employed Scikit Learn model after training in Python is essential for understanding the model’s behavior and making informed decisions. By inspecting the trained model, using model evaluation techniques, and leveraging model-specific attributes, you can gain insights into the employed model and its performance.