Evaluating a model
Throughout this course, you've been working on a project to classify heart disease using machine learning. You've successfully cleaned the dataset, performed feature engineering, and trained your model.
Here, you will employ the methods you have learned so far for model evaluation. You will evaluate a machine learning model using appropriate error metrics, visualize the evaluation results, and identify potential overfitting in preparation for deployment. By the end of this exercise, you will have gained a deeper understanding of model evaluation and visualization techniques.
- The trained logistic regression model is loaded as
model
KFold
andcross_val_score
are imported fromsklearn.model_selection
confusion_matrix
is imported fromsklearn.metrics
.- The variables
heart_disease_df_X
andheart_disease_df_y
have been already imported.
This exercise is part of the course
End-to-End Machine Learning
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Evaluate model using k-fold cross-validation
kf = ____(____=____)