1.
Analytical Method
It is a generic process combining the power of the Scientific
Method with the use of formal process to solve any type of
problem. It has these nine steps:
1. Identify the problem to solve.
2. Choose an appropriate process.
3. Use the process to hypothesize analysis or solution elements.
4. Design an experiments to test the hypothesis.
5. Perform the experiments.
6. Accept, reject, or modify the hypothesis.
7. Repeat steps 3, 4, 5, and 6 until the hypothesis is accepted.
8. Implement the solution.
9. Continuously improve the process as opportunities arise.

2.
Analytical approach
Analytical approach is an appropriate process to break a
problem down into the elements necessary to solve it. Each
element becomes a smaller and easier problem to solve.
According to Morgan Jones, former CIA analyst, An
analytical approach is also known as "structuring one's
analysis.”

3.
ENSEMBLE METHODS
Ensemble methods are used to create
stronger (More Accurate) classification
tree models. This is done by combining
weak classification tree models to create
stronger versions.

4.
ENSEMBLE METHODS
XLMiner offers three robust ensemble
methods:
• Bagging
• Boosting
• Random Trees

5.
ENSEMBLE METHODS
These methods differ in terms of :
 How data is selected from the weak dataset?
 How the weak models are generated?
 How the outputs are combined to form a
stronger classification tree model?
Specific ensemble methods may be more
appropriate in certain situations based on the
nature of the dataset (e.g., size, ability to
parallelize).

6.
ENSEMBLE METHODS
However, by using ensemble techniques, we can reduce
the bias and variance of a model. This reduces the
overall generalization error.
• Bias: Bias is an error which arises due to false
assumptions made in the learning phase of a model.
• Variance: Variance tells us how sensitive a model is to
small changes in the training data.

7.
Bagging
Bagging (bootstrap aggregating) is a simple but powerful
ensemble algorithm that facilitates the increased stability &
accuracy of classification models. It works by generating
multiple training datasets via random sampling with
replacement (RSR), applying the algorithm to each dataset,
and then taking the majority vote amongst the models to
determine data classifications.

8.
Bagging

9.
Bagging
Advantages
Reduces variance.
Helps to prevent over fitting.
Easily parallelized for application to large
datasets.

10.
Boosting
Boosting facilitates the conversion of weak learners (i.e. classifiers
with weak correlations) to strong learners (i.e. High correlated
classifiers).
Boosting creates strong classification tree models by training models
to concentrate on misclassified records from previous models; when
this is done, all classifiers are combined by a weighted majority vote.
This process places a higher weight to incorrectly classified records
while decreasing the weight of correct classifications. This effectively
forces subsequent models to place a greater emphasis on
misclassified records. The algorithm then computes the weighted
sum of votes for each class and assigns the best classification to the
record.

11.
Boosting

12.
Boosting
Disadvantages
It is not capable for parallelization.
Advantages
reduces bias and variance.

13.
Differences Between Bagging and Boosting

14.
Random Trees
Random Trees borrows the Bagging concept of
random feature selection to construct decision tree
models with controlled variance. The Random Tree
method uses training datasets to create multiple
decision trees and then using the mode from each
class to create a strong classifier.

15.
Random Trees
Advantages
reduces variance.
helps to prevent over fitting.
easily parallelized for application to large datasets.
The generation of models is faster than Bagging.

16.
How to Access Ensemble Methods in Excel?
• Launch Excel.
• In the toolbar, click XLMINER PLATFORM.
• In the ribbon's Data Mining section, click Classify.
• In the drop-down menu, hover the mouse cursors
over Classification Tree to reveal a sub-menu.
• Select Boosting, Bagging, Random Trees, or Single
Tree as needed.