Cross-validation is a crucial technique for assessing and improving the performance of machine learning models and is the next natural step for further analysis of the model.

I worked on setting up Python code for **K-fold cross-validation**, which involved splitting the dataset into K subsets (folds) to perform training and testing multiple times. In each iteration of the cross-validation loop, a different subset of the data is used as the test set, while the remaining data is used for training.

The code calculates the R-squared value for each fold of the cross-validation. The R-squared value is a measure of how well the model’s predictions match the actual log-transformed % DIABETIC values in the test set. The R-squared value ranges from 0 to 1, with higher values indicating a better fit of the model to the data.

Finally, the code calculates the average R-squared value across all the folds of cross-validation. This average R-squared value gives you an estimate of how well the quadratic regression model is performing on average across different subsets of the data.

So, in summary, the “model” in the code is a quadratic regression model that is trained and evaluated using K-fold cross-validation to assess its performance in predicting log-transformed % DIABETIC values based on the predictor variables % INACTIVE and % OBESE.

**I obtained an average R-squared value of 0.36 from the 5-fold cross-validation.**

While an R-squared value of 0.36 suggests that the model is capturing a significant portion of the variance, it also indicates that there is a substantial amount of unexplained variance in the data. This implies that factors other than % INACTIVE and % OBESE may contribute to the variation in % DIABETIC.

Based on this result, I will consider further model refinement or explore other predictors to improve the model’s explanatory power. Additionally, assess the residuals and other model diagnostics to ensure that the model assumptions are met and to identify potential areas for improvement.