This overview discusses the predictive analytical technique known as Random Forest Regression, a method of analysis that creates a set of Decision Trees from a randomly selected subset of the training set, and aggregates by averaging values from different decision trees to decide the final target value. This technique is useful to determine which predictors have a significant impact on the target values, e.g., the impact of average rainfall, city location, parking availability, distance from hospital, and distance from shopping on the price of a house, or the impact of years of experience, position and productive hours on employee salary. Random Forest Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. Random Forest Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.

1. Master the Art of Analytics
Basic Analytics for Citizen Data Scientists
T h e A u g m e n t e d A n a l y t i c s J o u r n e y
March - 2021

2. Random Forest Regression

3. Terminology
Introduction & Example
Standard Input/Tuning Parameters & Sample UI
Sample Output UI
Interpretation of Output
Limitations
Business Use Cases
Overview

4. Introduction – Random Forest Regression
Figure 1 – Structure of Random Forest Regression
Random Forest Regression creates a set
of Decision Trees from
a randomly selected subset of the
training set, and aggregates by averaging
values from different decision trees to
decide the final target value.
Predictors and Target Variable
Target Variable (usually denoted by Y)
represents the variable that will be
predicted and is also called Dependent
Variable, Response Variable or Outcome
Variable.
Predictor (usually denoted by X) is
sometimes called an Independent
Variable or Explanatory Variable, and is
the variable used to predict the Target
Variable Y.

5. Methodology
How Random Forest works:
1. Pick k data points at random from
the training set.
2. Build a decision tree associated to
these k data points.
3. Choose the number of trees – N,
that you want to build and repeat
steps 1 and 2.
4. For a new data point, make each
one of your N-trees tree predict the
value of y for the data point in
question and assign the new data
point to the average across all of
the predicted y values.

6. Sample Random Forest Regression
Here we perform Random Forest Regression analysis on independent variables: carpet area, buildup area, market distance, rainfall, city type and
target variable: House price
Independent
variables (Xi)
Target
Variable (Y) Model is a good fit as
Accuracy > 70%
Regression Statistics
Accuracy 78%
Root Mean Square
Error
179.23
Mean Absolute Error 92.20
House
Price
Carpet
Area
Build up
area
Market
distance
Rainfall City type
2027000 1624 2171 1 870 CAT A
6118000 986 1822 0.7 1160 CAT C
5916000 1627 1770 0.2 340 CAT A
4350000 1816 1154 2 1250 CAT B
8976000 1160 2000 0.1 1150 CAT B
7157000 1309 1807 0.4 420 CAT C
5934000 1543 1678 3 680 CAT A
6354000 2019 1543 1 1100 CAT B
• Root Mean Square Error: Square root of
the average of squared difference between
prediction and actual observation
• Mean Absolute Error: Average of the
difference between prediction and actual
observation.

7. Select the Target Variable
Build up Area
Carpet Area
House Price
Market Distance
Step
1
Select the Predictors
Build up Area
Carpet Area
House Price
Market Distance
Step
2
More than one
predictor can be
selected
Step 3
Number of Trees = 20
By default these parameters
should be set with the values
mentioned
Step 4
Display the output window containing following:
o Model summary
o Interpretation
o Residual plot
▪ Categorical predictors should be auto detected and converted to dummy/binary variables before applying regression
▪ Decision on selection of predictors depends on business knowledge and the correlation value between the target variable and predictors.
Standard Input/Tuning Parameters & Sample UI

8. Sample Output - Model Summary
Root Mean Square Error (RMSE): Square root of the average of squared differences between
prediction and actual observation. It is the standard deviation of residual error.
Mean Absolute Error (MAE): Average of the absolute differences between prediction and actual
observation.
Accuracy: Reveals appropriateness of the fit of the model, with
a value between 1 and 100. The closer the value to 100, the
better the model.
Root Mean Square Error 179.23
Mean Absolute Error 92.20
Used to identify the variation of errors from predicted to actual values. Lower Values (near to
zero) of RMSE and MAE represent a better fit of the regression model.

9. Sample Output - Interpretation
The Feature Importance chart reveals the impact of each Influencer on the Target Variable.

10. Sample Output - Plot
The Residual Plot is used to check the assumption of equal error variances and outliers
*See Interpretation sample for more details

11. Interpretation of Model Statistics
Accuracy
• Accuracy >70%
indicates the model is
a good fit for the data,
and that the predicted
values are reasonably
accurate
• Accuracy <70%
indicates that the model
is not a good fit for the
data, and the predicted
values are likely to have
significant errors
Root Mean Square
Error (RMSE):
• Square root of the
average of squared
differences between
the prediction and the
actual observation
(standard deviation of
residual error)
• Used to identify
variation from predicted
to actual values
• Lower values
(near zero) indicate a
better fit of regression
model
Mean Absolute
Error (MAE)
• Average of absolute
differences between
prediction and actual
observation
• Used to identify
variation of errors
from predicted to
actual values
• Lower values
(near zero)
indicate better fit of
regression model
Feature Important
• Values are used to check
the impact of each
Influencer (Predictor) on
the Target Variable
• Random Forest is a
useful technique to
determine Feature
Importance
• Feature importance is
useful in selecting
appropriate predictors
affecting the target
which is helpful in
training the model.

12. Interpretation of Plots - Residual vs. Fit Plot
Indicates the scattered plot of standardized residuals on Y axis and predicted (fitted) values on X axis
Note: The red data point in figure 1 is an outlier and should be removed from data before interpreting the model
Used to detect the unequal residual variances and Outliers in data

13. Limitations
• Random Forest Regression is limited
to predicting numeric output so
dependent variable must be numeric
in nature.
• The minimum sample size should be
at least 20 cases per independent
variable.
• Residuals should be time
independent as illustrated in the
image..
Time independent error (fairly constant over time and within a certain range)

14. Limitations
• Target/Independent variables should be normally
distributed
A normal distribution is an arrangement of a dataset in
which most values are midrange and the rest taper off
symmetrically toward either extreme. It will look like a
bell curve as shown in figure 1 on the right
• Outliers in data (target as well as Independent Variables)
can affect the analysis, and must be removed.
Outliers are the observations lying outside overall pattern
of distribution as shown in figure 2 in right. These
extreme values/outliers can be replaced with 1st or 99th
percentile values to improve model accuracy
Outliers
Figure 1
Figure 2

15. Business Use Case - House Price
Business Problem
A real-estate brokerage company wants to measure the impact of locality, the number of rooms, the
area(sq. yards) etc. on a house price. The goal of this statistical analysis is to help us understand the
relationship between house features and how these variables are used to predict house price.
Target
House Price
Predictors
Area with carpet, Rainfall, city, parking, distance from hospital, distance from shopping, etc
Business Benefit
• The business can determine which predictors have a significant impact on house price.
• Pricing strategies and recommendations will be more accurate and result in quicker sales.
• If the number of rooms or the distance from shopping or schools are significant factors, these factors
are given more focus when searching for a house that fits a client budget and affects profit.

16. Business Use Case - Agriculture
Business Problem
An agriculture business wants to measure the impact of weather, market price, quality of crop, land
used etc. on the crop price.
Input Data
Predictor/Independent Variables
• Weather
• Demand
• Crop health
Dependent Variable
Crop Price
Business Benefit
• Business can clarify which factors have a significant impact on crop price.
• Pricing strategies can be refined to improve accuracy and meet targeted crop pricing and revenue.
• If crop health and climate are significant factors, these factors would receive more focus when
deciding crop price.

17. Business Use Case - Compensation Policies
Business Problem
A business wishes to measure the salary of employee based on position, experience, degree, level,
productive hours etc.
Input Data
Predictor/Independent Variables
• Position
• Years of experience
• Productive hours
Dependent Variable
Salary of Employee
Business Benefit
• The business can clarify which predictors have a significant impact on employee salary.
• Salary policies and strategies can more accurately reflect employee value and targeted salaries.
• If productive hours and experience are significant factors, these factors would be given more focus
when developing salary policies.

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