Forecast evaluation & experimentation
In this exercise, you'll evaluate the forecast model's performance to explore the use cases of experimentation.
The merged forecast (fc), combining predictions with actual test results, is preloaded. Evaluation functions (mape, rmse, coverage) and pandas (as pd) are also ready for use. Here's a quick reference for the functions:
def mape(y, yhat):
mape = mean(abs(y - yhat) / y)
return mape
def rmse(y, yhat):
rmse = (mean((y - yhat) ** 2)) ** 0.5
return rmse
def coverage(y, lower, upper):
coverage = sum((y <= upper) & (y >= lower)) / len(y)
return coverage
First, compute performance metrics for the model. Then, answer a question about the goals of experimentation in forecasting.
Acest exercițiu face parte din cursul
Designing Forecasting Pipelines for Production
Exercițiu interactiv practic
Încearcă acest exercițiu completând acest cod de exemplu.
performance_metrics = []
# Loop through models and calculate metrics
for model in ["LGBMRegressor", "XGBRegressor", "LinearRegression"]:
performance_metrics.append({
"model": model,
"mape": ____(fc["y"], fc[model]),
"rmse": ____(fc["y"], fc[____]),
"coverage": ____(fc["y"], fc[f"{model}-lo-95"], fc[f"{model}-hi-95"])
})
# Create DataFrame and sort by RMSE
fc_performance = pd.DataFrame(performance_metrics).sort_values("____")
print(fc_performance)