Estimating housing prices

It's time to apply our knowledge to a real-world problem. Let's apply all these principles to estimate house prices. This is one of the most popular examples that is used to understand regression, and it serves as a good entry point. This is intuitive and relatable, hence making it easier to understand the concepts before we perform more complex things in machine learning. We will use a decision tree regressor with AdaBoost to solve this problem.

Getting ready

A decision tree is a tree where each node makes a simple decision that contributes to the final output. The leaf nodes represent the output values, and the branches represent the intermediate decisions that were made, based on input features. AdaBoost stands for adaptive boosting, and this is a technique that is used to boost the accuracy of the results from another system. This combines the outputs from different versions of the algorithms, called weak learners, using a weighted summation to get the final output. The information that's collected at each stage of the AdaBoost algorithm is fed back into the system so that the learners at the latter stages focus on training samples that are difficult to classify. In this way, it increases the accuracy of the system.

Using AdaBoost, we fit a regressor on the dataset. We compute the error and then fit the regressor on the same dataset again, based on this error estimate. We can think of this as fine-tuning of the regressor until the desired accuracy is achieved. You are given a dataset that contains various parameters that affect the price of a house. Our goal is to estimate the relationship between these parameters and the house price so that we can use this to estimate the price given unknown input parameters.

How to do it...

Let's see how to estimate housing prices in Python:

1. Create a new file called housing.py and add the following lines:

import numpy as np
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn import datasets
from sklearn.metrics import mean_squared_error, explained_variance_score
from sklearn.utils import shuffle
import matplotlib.pyplot as plt

2. There is a standard housing dataset that people tend to use to get started with machine learning. You can download it at https://archive.ics.uci.edu/ml/machine-learning-databases/housing/. We will be using a slightly modified version of the dataset, which has been provided along with the code files.
   The good thing is that scikit-learn provides a function to directly load this dataset:

housing_data = datasets.load_boston()

Each data point has 12 input parameters that affect the price of a house. You can access the input data using housing_data.data and the corresponding price using housing_data.target. The following attributes are available:

• crim: Per capita crime rate by town
• zn: Proportion of residential land zoned for lots that are over 25,000 square feet
• indus: Proportion of non-retail business acres per town
• chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
• nox: Nitric oxides concentration (parts per ten million)
• rm: Average number of rooms per dwelling
• age: Proportion of owner-occupied units built prior to 1940
• dis: Weighted distances to the five Boston employment centers
• rad: Index of accessibility to radial highways
• tax: Full-value property-tax rate per $10,000
• ptratio: Pupil-teacher ratio by town
• lstat: Percent of the lower status of the population
• target: Median value of owner-occupied homes in $1000

Of these, target is the response variable, while the other 12 variables are possible predictors. The goal of this analysis is to fit a regression model that best explains the variation in target.

3. Let's separate this into input and output. To make this independent of the ordering of the data, let's shuffle it as well:

X, y = shuffle(housing_data.data, housing_data.target, random_state=7)

The sklearn.utils.shuffle() function shuffles arrays or sparse matrices in a consistent way to do random permutations of collections. Shuffling data reduces variance and makes sure that the patterns remain general and less overfitted. The random_state parameter controls how we shuffle data so that we can have reproducible results.

4. Let's divide the data into training and testing. We'll allocate 80% for training and 20% for testing:

num_training = int(0.8 * len(X))
X_train, y_train = X[:num_training], y[:num_training]
X_test, y_test = X[num_training:], y[num_training:]

Remember, machine learning algorithms, train models by using a finite set of training data. In the training phase, the model is evaluated based on its predictions of the training set. But the goal of the algorithm is to produce a model that predicts previously unseen observations, in other words, one that is able to generalize the problem by starting from known data and unknown data. For this reason, the data is divided into two datasets: training and test. The training set is used to train the model, while the test set is used to verify the ability of the system to generalize.

5. We are now ready to fit a decision tree regression model. Let's pick a tree with a maximum depth of 4, which means that we are not letting the tree become arbitrarily deep:

dt_regressor = DecisionTreeRegressor(max_depth=4)
dt_regressor.fit(X_train, y_train)

The DecisionTreeRegressor function has been used to build a decision tree regressor.

6. Let's also fit the decision tree regression model with AdaBoost:

ab_regressor = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=400, random_state=7)
ab_regressor.fit(X_train, y_train)

The AdaBoostRegressor function has been used to compare the results and see how AdaBoost really boosts the performance of a decision tree regressor.

7. Let's evaluate the performance of the decision tree regressor:

y_pred_dt = dt_regressor.predict(X_test)
mse = mean_squared_error(y_test, y_pred_dt)
evs = explained_variance_score(y_test, y_pred_dt)
print("#### Decision Tree performance ####")
print("Mean squared error =", round(mse, 2))
print("Explained variance score =", round(evs, 2))

First, we used the predict() function to predict the response variable based on the test data. Next, we calculated mean squared error and explained variance. Mean squared error is the average of the squared difference between actual and predicted values across all data points in the input. The explained variance is an indicator that, in the form of proportion, indicates how much variability of our data is explained by the model in question.

8. Now, let's evaluate the performance of AdaBoost:

y_pred_ab = ab_regressor.predict(X_test)
mse = mean_squared_error(y_test, y_pred_ab)
evs = explained_variance_score(y_test, y_pred_ab)
print("#### AdaBoost performance ####")
print("Mean squared error =", round(mse, 2))
print("Explained variance score =", round(evs, 2))

Here is the output on the Terminal:

#### Decision Tree performance ####
Mean squared error = 14.79
Explained variance score = 0.82

#### AdaBoost performance ####
Mean squared error = 7.54
Explained variance score = 0.91

The error is lower and the variance score is closer to 1 when we use AdaBoost, as shown in the preceding output.

How it works...

DecisionTreeRegressor builds a decision tree regressor. Decision trees are used to predict a response or class y, from several input variables; x1, x2,…,xn. If y is a continuous response, it's called a regression tree, if y is categorical, it's called a classification tree. The algorithm is based on the following procedure: We see the value of the input x_i at each node of the tree, and based on the answer, we continue to the left or to the right branch. When we reach a leaf, we will find the prediction. In regression trees, we try to divide the data space into tiny parts, where we can equip a simple different model on each of them. The non-leaf part of the tree is just the way to find out which model we will use for predicting it.

A regression tree is formed by a series of nodes that split the root branch into two child branches. Such subdivision continues to cascade. Each new branch, then, can go in another node, or remain a leaf with the predicted value.

There's more...

An AdaBoost regressor is a meta-estimator that starts by equipping a regressor on the actual dataset and adding additional copies of the regressor on the same dataset, but where the weights of instances are adjusted according to the error of the current prediction. As such, consecutive regressors look at difficult cases. This will help us compare the results and see how AdaBoost really boosts the performance of a decision tree regressor.

See also

• Scikit-learn's official documentation of the DecisionTreeRegressor function (https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html)
• Scikit-learn's official documentation of the AdaBoostRegressor function (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html)