learn-co-students / dsc-model-fit-linear-regression-lab-online-ds-sp-000

Model Fit in Linear Regression - Lab

Introduction

In this lab, you'll learn how to evaluate your model results and you'll learn how to select the appropriate features using stepwise selection.

Objectives

You will be able to:

• Use stepwise selection methods to determine the most important features for a model
• Use recursive feature elimination to determine the most important features for a model

The Ames Housing Data once more

import pandas as pd
import numpy as np

ames = pd.read_csv('ames.csv')

continuous = ['LotArea', '1stFlrSF', 'GrLivArea', 'SalePrice']
categoricals = ['BldgType', 'KitchenQual', 'SaleType', 'MSZoning', 'Street', 'Neighborhood']

ames_cont = ames[continuous]

# log features
log_names = [f'{column}_log' for column in ames_cont.columns]

ames_log = np.log(ames_cont)
ames_log.columns = log_names

# normalize (subract mean and divide by std)

def normalize(feature):
    return (feature - feature.mean()) / feature.std()

ames_log_norm = ames_log.apply(normalize)

# one hot encode categoricals
ames_ohe = pd.get_dummies(ames[categoricals], prefix=categoricals, drop_first=True)

preprocessed = pd.concat([ames_log_norm, ames_ohe], axis=1)

Perform stepwise selection

The function for stepwise selection is copied below. Use this provided function on your preprocessed Ames Housing data.

import statsmodels.api as sm

def stepwise_selection(X, y, 
                       initial_list=[], 
                       threshold_in=0.01, 
                       threshold_out = 0.05, 
                       verbose=True):
    """ 
    Perform a forward-backward feature selection 
    based on p-value from statsmodels.api.OLS
    Arguments:
        X - pandas.DataFrame with candidate features
        y - list-like with the target
        initial_list - list of features to start with (column names of X)
        threshold_in - include a feature if its p-value < threshold_in
        threshold_out - exclude a feature if its p-value > threshold_out
        verbose - whether to print the sequence of inclusions and exclusions
    Returns: list of selected features 
    Always set threshold_in < threshold_out to avoid infinite looping.
    See https://en.wikipedia.org/wiki/Stepwise_regression for the details
    """
    included = list(initial_list)
    while True:
        changed=False
        # forward step
        excluded = list(set(X.columns)-set(included))
        new_pval = pd.Series(index=excluded, dtype='float64')
        for new_column in excluded:
            model = sm.OLS(y, sm.add_constant(pd.DataFrame(X[included+[new_column]]))).fit()
            new_pval[new_column] = model.pvalues[new_column]
        best_pval = new_pval.min()
        if best_pval < threshold_in:
            best_feature = new_pval.idxmin()
            included.append(best_feature)
            changed=True
            if verbose:
                print('Add  {:30} with p-value {:.6}'.format(best_feature, best_pval))

        # backward step
        model = sm.OLS(y, sm.add_constant(pd.DataFrame(X[included]))).fit()
        # use all coefs except intercept
        pvalues = model.pvalues.iloc[1:]
        worst_pval = pvalues.max() # null if pvalues is empty
        if worst_pval > threshold_out:
            changed=True
            worst_feature = pvalues.idxmax()
            included.remove(worst_feature)
            if verbose:
                print('Drop {:30} with p-value {:.6}'.format(worst_feature, worst_pval))
        if not changed:
            break
    return included

# Your code here

Build the final model again in Statsmodels

# Your code here

Use Feature ranking with recursive feature elimination

Use feature ranking to select the 5 most important features

# Your code here

Fit the linear regression model again using the 5 selected columns

# Your code here

Now, predict $\hat y$ using your model. You can use .predict() in scikit-learn.

# Your code here

Now, using the formulas of R-squared and adjusted R-squared below, and your Python/numpy knowledge, compute them and contrast them with the R-squared and adjusted R-squared in your statsmodels output using stepwise selection. Which of the two models would you prefer?

$SS_{residual} = \sum (y - \hat{y})^2 $

$SS_{total} = \sum (y - \bar{y})^2 $

$R^2 = 1- \dfrac{SS_{residual}}{SS_{total}}$

$R^2_{adj}= 1-(1-R^2)\dfrac{n-1}{n-p-1}$

# Your code here

# r_squared is 0.239434  
# adjusted_r_squared is 0.236818

Level up (Optional)

• Perform variable selection using forward selection, using this resource: https://planspace.org/20150423-forward_selection_with_statsmodels/. Note that this time features are added based on the adjusted R-squared!
• Tweak the code in the stepwise_selection() function written above to just perform forward selection based on the p-value

Summary

Great! You practiced your feature selection skills by applying stepwise selection and recursive feature elimination to the Ames Housing dataset!

dsc-model-fit-linear-regression-lab-online-ds-sp-000's Issues

Potential Issue: argmax() vs idxmax()

In the stepwise_selection() function,

worst_feature = pvalues.argmax()

should be :

worst_feature = pvalues.idxmax()

as .argmax() is deprecated and throws an error in the lab: ValueError: list.remove(x): x not in list