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.
Den här övningen är en del av kursen
Designing Forecasting Pipelines for Production
Interaktiv övning med praktiskt arbete
Testa den här övningen genom att slutföra den här exempelkoden.
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)