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.

Deze oefening maakt deel uit van de cursus

Designing Forecasting Pipelines for Production

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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)

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