Title: Create Baseline Regression Model
Slug: create_baseline_regression_model
Summary: How to create a baseline regression model in scikit-learn for machine learning in Python.
Date: 2017-09-14 12:00
Category: Machine Learning
Tags: Model Evaluation
Authors: Chris Albon

Preliminaries



In [23]:

    
# Load libraries
from sklearn.datasets import load_boston
from sklearn.dummy import DummyRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

Load Boston Housing Dataset



In [24]:

    
# Load data
boston = load_boston()

# Create features
X, y = boston.data, boston.target

Split Data Into Training And Test Set



In [25]:

    
# Make test and training split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

Create Dummy Regression Always Predicts The Mean Value Of Target



In [26]:

    
# Create a dummy regressor
dummy_mean = DummyRegressor(strategy='mean')

# "Train" dummy regressor
dummy_mean.fit(X_train, y_train)









    Out[26]:





DummyRegressor(constant=None, quantile=None, strategy='mean')

Create Dummy Regression Always Predicts A Constant Value



In [27]:

    
# Create a dummy regressor
dummy_constant = DummyRegressor(strategy='constant', constant=20)

# "Train" dummy regressor
dummy_constant.fit(X_train, y_train)









    Out[27]:





DummyRegressor(constant=array(20), quantile=None, strategy='constant')

Evaluate Performance Metric



In [28]:

    
# Get R-squared score
dummy_constant.score(X_test, y_test)









    Out[28]:





-0.065105020293257265