Cross-validation without shuffling

Now, re-run your model fit using block cross-validation (without shuffling all datapoints). In this case, neighboring time-points will be kept close to one another. How do you think the model predictions will look in each cross-validation loop?

An instance of the Linear regression model object is available in your workspace. Also, the arrays X and y (training data) are available too.

This exercise is part of the course

Machine Learning for Time Series Data in Python

View Course

Exercise instructions

Instantiate another cross-validation object, this time using KFold cross-validation with 10 splits and no shuffling.
Iterate through this object to fit a model using the training indices and generate predictions using the test indices.
Visualize the predictions across CV splits using the helper function (visualize_predictions()) we've provided.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create KFold cross-validation object
from sklearn.model_selection import KFold
cv = ____(n_splits=____, shuffle=____)

# Iterate through CV splits
results = []
for tr, tt in cv.split(X, y):
    # Fit the model on training data
    model.fit(____)
    
    # Generate predictions on the test data and collect
    prediction = model.predict(____)
    results.append((prediction, tt))
    
# Custom function to quickly visualize predictions
visualize_predictions(results)

Edit and Run Code