Course Content

Linear Regression with Python

## Linear Regression with Python

# Interpolation vs Extrapolation

In the previous chapter, we noticed that our predictions using different models are getting more diverse at the edges.

To be more precise, the predictions are getting weird at the moment we are past the range of values from the training set. Predicting values outside the training set's range is called **extrapolation**, and predicting values inside the range is **interpolation**.

The Regression does not handle the extrapolation well. It is used for interpolation and can yield absurd predictions when new instances are out of the training set's range.

## Confidence intervals

Using the `OLS`

class, you can also get the confidence intervals for the regression line at any point. But the syntax is a bit complicated:

Where `alpha`

is a confidence level, usually set to `0.05`

.

Using the above code, you will get lower and upper bounds of the regression line's confidence interval at the point `X_new_tilde`

(or an array of upper and lower bounds if `X_new_tilde`

is an array)

Let's use it to plot the regression line along with its confidence interval

`import pandas as pd import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm from sklearn.preprocessing import PolynomialFeatures # Import PolynomialFeatures class file_link = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/poly.csv' df = pd.read_csv(file_link) n = 4 # A degree of the polynomial regression X = df[['Feature']] # Assign X as a DataFrame y = df['Target'] # Assign y X_tilde = PolynomialFeatures(n).fit_transform(X) # Get X_tilde regression_model = sm.OLS(y, X_tilde).fit() # Initialize and train the model X_new = np.linspace(-0.1, 1.5, 80) # 1-d array of new feature values X_new_tilde = PolynomialFeatures(n).fit_transform(X_new.reshape(-1,1)) # Transform X_new for predict() method y_pred = regression_model.predict(X_new_tilde) lower = regression_model.get_prediction(X_new_tilde).summary_frame(0.05)['mean_ci_lower'] # Get lower bound for each point upper = regression_model.get_prediction(X_new_tilde).summary_frame(0.05)['mean_ci_upper'] # get upper bound for each point plt.scatter(X, y) # Build a scatterplot plt.plot(X_new, y_pred) # Build a Polynomial Regression graph plt.fill_between(X_new, lower, upper, alpha=0.4)`

Without knowing the distribution of a target, we can't find the exact regression line. All we do is try to approximate it based on our data. The **confidence interval** of the regression line is the interval in which the exact regression line lies with the confidence level `alpha`

.

You can see that the interval grows larger and larger as it gets further from the training set's range.

Note

The confidence intervals are built assuming we correctly chose the model (e.g., Simple Linear Regression or Polynomial Regression of degree 4).

If the model is chosen poorly, the confidence interval is unreliable, and so is the line itself. You will learn how to select the best model in the following section.

Everything was clear?