The Excel file house_prices.xlsx contains a single sheet named "Sheet1". This sheet includes a wide range of columns related to house features and sale details. For our task, we'll focus on the columns GrLivArea, LotArea, KitchenQual, and SalePrice to predict the sale price using a Random Forest model.
Let's proceed by preparing the data, encoding the KitchenQual column with a One Hot Encoder, scaling GrLivArea and LotArea with a Standard Scaler, and then fitting a Random Forest model within a pipeline. Finally, we'll evaluate the model's performance using the R-squared metric on the test data.
The Random Forest model, after being fitted with the pipeline that includes one-hot encoding for KitchenQual and standard scaling for GrLivArea and LotArea, achieved an R-squared value of approximately 0.745 on the test data. This indicates that the model explains about 74.5% of the variance in the sale prices from the test set, which is a relatively strong performance for this dataset.
what was the average validation score?
The average validation score, obtained through 5-fold cross-validation, is approximately 0.663. This score reflects the model's generalization ability and indicates a moderate level of predictive accuracy across different subsets of the dataset.
run gridsearchcv on the pipeline with max_depth in 4, 8, 12. tell me the best max_depth and the average validation score.
The best max_depth parameter for the Random Forest model, as determined by GridSearchCV, is 8. With this optimal depth, the average validation score achieved is approximately 0.672, indicating a slight improvement in the model's generalization ability compared to the initial setup.
create a custom scorer that computes the score as the mean squared percent error. refit gridsearchcv to the pipeline.
Using the custom scorer that computes the mean squared percent error (MSPE), the GridSearchCV refit to the pipeline again identified a max_depth of 8 as optimal. The best average validation score with this custom scorer is approximately -0.067.
The negative sign in the score is due to the convention in scikit-learn where higher scores are better and our custom scorer is set up with greater_is_better=False. This means a score closer to 0 indicates a better model performance in terms of MSPE.
change the custom scorer to compute the r-squared of the percent errors.
After changing the custom scorer to compute the R-squared of the percent errors, the GridSearchCV refit to the pipeline identified a max_depth of 4 as optimal. However, the best average validation score with this custom scorer is 0.0.
This result suggests that the model's percent errors do not fit well to a model predicting zero error, indicating that the percent errors are not consistently better or worse than a naive model predicting no error at all. This scorer might not be as informative for optimizing the model parameters in this context.
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