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Performance estimation for tip prediction

In the previous exercises, you prepared a reference and analysis set for the NYC Green Taxi dataset. In this one, you will use that data to estimate the model's performance in production.

First, you must initialize the DLE algorithm with the provided parameters and then plot the results.

The reference and analysis set is already loaded and saved in the reference and analysis variables. Additionally, nannyml is also already imported.

Diese Übung ist Teil des Kurses

<Kurs>Monitoring Machine Learning in Python</Kurs>
Kurs ansehen

Übungsanweisungen

  • Initiate the DLE algorithm with daily chunk period, tip_amount as a y_true , and MSE metric.
  • Fit reference set to the DLE estimator, estimate performance for analysis set and store the output in the results variable.
  • Visualize the results using plot() and show() methods.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

estimator = nannyml.DLE(y_pred='y_pred',
    timestamp_column_name='lpep_pickup_datetime',
    feature_column_names=features,
    chunk_period='d',
    y_true='tip_amount',
    metrics=['mse'])

# Fit the reference data to the DLE algorithm
estimator.____(____)

# Estimate the performance on the analysis data
results = estimator.____(____)

# Plot and show the results
____.____().____()
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