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.
Bu egzersiz
Machine Learning for Time Series Data in Python
kursunun bir parçasıdırEgzersiz talimatları
- Review
new_inputsin the shell. - Reshape
new_inputsappropriately to generate predictions. - Run the given code to visualize the predictions.
Uygulamalı interaktif egzersiz
Bu örnek kodu tamamlayarak bu egzersizi bitirin.
# 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()