Machine Learning

Random Forest for Regression[Case Study]

Using Random Forests for Regression Problems

Introduction :

The goal of the blogpost is to equip beginners with basics of Random Forest Regressor algorithm and quickly help them to build their first model.  We will mainly focus on the modeling side of it . The data cleaning and preprocessing parts would be covered in detail in an upcoming post.

Ensemble methods are supervised learning models which combine the predictions of multiple smaller models to improve predictive power and generalization.

The smaller models that combine to make the ensemble model are referred to as base models. Ensemble methods often result in considerably higher performance than any of the individual base models could achieve.

Two popular families of ensemble methods

BAGGING
Several estimators are built independently on subsets of the data and their predictions are averaged. Typically the combined estimator is usually better than any of the single base estimator.
Bagging can reduce variance with little to no effect on bias.

ex: Random Forests
BOOSTING
Base estimators are built sequentially. Each subsequent estimator focuses on the weaknesses of the previous estimators. In essence several weak models “team up” to produce a powerful ensemble model. (We will discuss these later this week.)

Boosting can reduce bias without incurring higher variance.

ex: Gradient Boosted Trees, AdaBoost

Bagging

The ensemble method we will be using today is called bagging, which is short for bootstrap aggregating.

Bagging builds multiple base models with resampled training data with replacement. We train kk base classifiers on k different samples of training data. Using random subsets of the data to train base models promotes more differences between the base models.

We can use the BaggingRegressor class to form an ensemble of regressors. One such Bagging ressor algorithm is random forest regressor. A random forest regressor is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default).

Random Forest Regressors uses some kind of splitting criterion to measure the quality of a split. Supported criteria are “mse” for the mean squared error, which is equal to variance reduction as feature selection criterion, and “mae” for the mean absolute error.

Enough of theory , let’s start with implementation.

Problem Statement :

To predict the median prices of homes located in   boston area given other attributes of the house.

Data details

Boston House Prices dataset
===========================

Notes
——
Data Set Characteristics:

:Number of Instances: 506

:Number of Attributes: 13 numeric/categorical predictive

:Median Value (attribute 14) is usually the target

:Attribute Information (in order):
– CRIM     per capita crime rate by town
– ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
– 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 10 million)
– RM       average number of rooms per dwelling
– AGE      proportion of owner-occupied units built prior to 1940
– DIS      weighted distances to five Boston employment centres
– RAD      index of accessibility to radial highways
– TAX      full-value property-tax rate per $10,000
– PTRATIO  pupil-teacher ratio by town
– B        1000(Bk – 0.63)^2 where Bk is the proportion of blacks by town
– LSTAT    % lower status of the population
– MEDV     Median value of owner-occupied homes in $1000’s

:Missing Attribute Values: None

:Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset.
http://archive.ics.uci.edu/ml/datasets/Housing

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. ‘Hedonic prices and the demand for clean air’, J. Environ. Economics & Management,
vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, ‘Regression diagnostics …’, Wiley, 1980.   N.B. Various transformations are used in the table on
pages 244-261 of the latter.

The Boston house-price data has been used in many machine learning papers that address regression
problems.
Tools used :

Pandas , Numpy , Matplotlib , scikit-learn

Python Implementation with code :

Import necessary libraries

Import the necessary modules from specific libraries.

import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import datasets
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor

Load the data set

Use pandas module to read the taxi data from the file system. Check few records of the dataset.

# #############################################################################

# Load data

boston = datasets.load_boston()

print(boston.data.shape, boston.target.shape)

print(boston.feature_names)

(506, 13) (506,)
['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO' 'B' 'LSTAT']
data = pd.DataFrame(boston.data,columns=boston.feature_names)
data = pd.concat([data,pd.Series(boston.target,name='MEDV')],axis=1)
data.head()
CRIM       ZN           INDUS     CHAS      NOX        RM          AGE        DIS          RAD        TAX         PTRATIO                B              LSTAT     MEDV

0              0.00632   18.0         2.31         0.0           0.538       6.575       65.2         4.0900     1.0           296.0       15.3         396.90                4.98         24.0

1              0.02731   0.0           7.07         0.0           0.469       6.421       78.9         4.9671     2.0           242.0       17.8         396.90                9.14         21.6

2              0.02729   0.0           7.07         0.0           0.469       7.185       61.1         4.9671     2.0           242.0       17.8         392.83                4.03         34.7

3              0.03237   0.0           2.18         0.0           0.458       6.998       45.8         6.0622     3.0           222.0       18.7         394.63                2.94         33.4

4              0.06905   0.0           2.18         0.0           0.458       7.147       54.2         6.0622     3.0           222.0       18.7         396.90                5.33         36.2

Select the predictor and target variables

X = data.iloc[:,:-1]
y = data.iloc[:,-1]

Train test split :

x_training_set, x_test_set, y_training_set, y_test_set = train_test_split(X,y,test_size=0.10,
random_state=42,
shuffle=True)

Training / model fitting :
Fit the model to selected supervised data

n_estimators=100
# Fit regression model
# Estimate the score on the entire dataset, with no missing values
model = RandomForestRegressor(random_state=0, n_estimators=n_estimators)
model.fit(x_training_set, y_training_set)

Model parameters study :

The coefficient R^2 is defined as (1 – u/v), where u is the residual sum of squares ((y_true – y_pred) ** 2).sum() and v is the total sum of squares ((y_true – y_true.mean()) ** 2).sum().

from sklearn.metrics import mean_squared_error, r2_score
model_score = model.score(x_training_set,y_training_set)
# Have a look at R sq to give an idea of the fit ,
# Explained variance score: 1 is perfect prediction
print(“ coefficient of determination R^2 of the prediction.: ',model_score)
y_predicted = model.predict(x_test_set)

# The mean squared error
print("Mean squared error: %.2f"% mean_squared_error(y_test_set, y_predicted))

# Explained variance score: 1 is perfect prediction
print('Test Variance score: %.2f' % r2_score(y_test_set, y_predicted))
Coefficient of determination R^2 of the prediction :  0.982022598521334
Mean squared error: 7.73
Test Variance score: 0.88

Accuracy report with test data :
Let’s visualise the goodness of the fit with the predictions being visualised by a line

# So let's run the model against the test data
from sklearn.model_selection import cross_val_predict
fig, ax = plt.subplots()
ax.scatter(y_test_set, y_predicted, edgecolors=(0, 0, 0))
ax.plot([y_test_set.min(), y_test_set.max()], [y_test_set.min(), y_test_set.max()], 'k--', lw=4)
ax.set_xlabel('Actual')
ax.set_ylabel('Predicted')
ax.set_title("Ground Truth vs Predicted")
plt.show()

Conclusion :

We can see that our R2 score and MSE are both very good. This means that we have found a best fitting model to predict the median price value of a house. There can be further improvement to the metric by doing some preprocessing before fitting the data. However the task for the post was to provide you sufficient knowledge to implement your first model. You can build over the existing pipeline and report your accuracies.

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Sudhanshu Kumar
Data Scientist at Verizon Labs

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