Predicting using a regression model

Now that you've fit a model with the California housing data, lets see what predictions it generates on some new data. You can investigate the underlying relationship that the model has found between inputs and outputs by feeding in a range of numbers as inputs and seeing what the model predicts for each input.

A 1-D array new_inputs consisting of 100 "new" values for "MedHouseVal" (median house value) is available in your workspace along with the model you fit in the previous exercise.

Diese Übung ist Teil des Kurses

Machine Learning for Time Series Data in Python

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Anleitung zur Übung

Review new_inputs in the shell.
Reshape new_inputs appropriately to generate predictions.
Run the given code to visualize the predictions.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Generate predictions with the model using those inputs
predictions = ____

# Visualize the inputs and predicted values
plt.scatter(new_inputs, predictions, color='r', s=3)
plt.xlabel('inputs')
plt.ylabel('predictions')
plt.show()

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