Visualizing predicted values
When dealing with time series data, it's useful to visualize model predictions on top of the "actual" values that are used to test the model.
In this exercise, after splitting the data (stored in the variables X and y) into training and test sets, you'll build a model and then visualize the model's predictions on top
of the testing data in order to estimate the model's performance.
Den här övningen är en del av kursen
Machine Learning for Time Series Data in Python
Interaktiv övning med praktiskt arbete
Testa den här övningen genom att slutföra den här exempelkoden.
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
# Split our data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(____, ____,
train_size=.8, shuffle=False)
# Fit our model and generate predictions
model = Ridge()
model.fit(____, ____)
predictions = model.predict(____)
score = r2_score(y_test, predictions)
print(score)