Predictive Modeling and Predictive Models
A predictive model is a system created and used to perform prediction.
Predictive models can predict or forecast variety of things and events.
For example, future share prices, credit defaults, insurance claims,
customer ordering products, and so on.
Predictive models are developed from past historical data or
from purposely collected data through sampling.
Typical examples may include;
Insurance
Annual insurance policy applications and claims records can be used
to develop models that can predict probability (or level of risk)
of insurance claims. Predictive models use demographic and financial
information of policy holders along with characteristics of
insured objects in determining risk levels.
For more, please read Insurance Risk Modeling.
Credit loans
Predicting default risk for credit loan applications is another use of
predictive models.
Data collected from past customer loans, including demographic and
financial information of borrowers, can be used to build predictive models
that can predict likelihood of loans being defaulted.
For more, please read Credit Risk Modeling.
Marketing
Predictive modeling can be used for various tasks. For example,
from past customer purchasing records, you can develop models
that can select customers who are likely to buy your new products.
Another example is customer churn detection. Using past customer
information, models that can predict customers who are likely to
churn near future. This can be very useful in retention marketing.
For more, please read Customer Churn Modeling.
* For predictive modeling software, please read Data Mining Software.
Predictive Modeling Software Tools
Predictive modeling is done automatically by computer software
that can learn patterns from data.
CMSR Data Miner supports powerful predictive modeling tools.
Users can build models with the help of intuitive model visualization
tools. Application of models is very easy. Users can apply models directly
to user data using built-in database interface tools.
CMSR comes with the following predictive modeling
software programs;
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Neural Network
Neural network is a predictive model which is based on the architecture of,
say, our brains. It can be used to predict both numerical values and
categorical classifications. Generally speaking, neural net offers most
accurate and versatile predictive models.
For more, please read
Neural Network.
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Decision Tree
Decision tree develops predictive models based on recursive segmentation.
Decision tree models have tree-like structures.
As the rule, decision is made based on the democratic principle:
the winner takes all. If a category of a decision node has the
largest number of cases, it will be the predicted category.
Of course, this leads to certain limitations!
To overcome this, StarProbe data miner also uses probability.
For more, please read
Decision Tree Classifier Software
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Regression
Compared to above methods, regression may be very limiting and inflexible, since
all categorical information should be encoded into numerical variables.
However, regression can be very useful in developing mathematically oriented
models with simple variable sets. Especially, time-series regression
analytics are very useful in
balanced scorecard applications.
* For predictive modeling software, please read Data Mining Software.
How can you develop predictive models?
To learn more about predictive modeling,
please read The Cookbook for Predictive Analytics.
Key requirement for predictive modeling
The most important factor that can lead to successful implementation
of predictive modeling is the availability of "predictive" information in your data.
It is noted that predictive models are statistically-developed patterns
extracted out of past historical data or purposely collected
sampling survey data. With proper data representing predictive patterns
of application domains, accurate predictive models can be developed
quite easily.
For more, please read
Cookbook for Predictive Analytics.
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Neural Network and Deep Learning Style Modeling
Neural networks are very robust and powerful. It can learn very fine details of information.
This can be a problem if statistical patterns are desired as in predictive modeling.
Large networks with a large number of input and hidden layer nodes can lead to overfitting.
A better modeling method is to decompose into multiple models with a fewer number of
input and hidden layer nodes. Then integrate models using higher level neural network(s).
This deep learning style modeling can produce better predictive result.
For more, please read
RME-EP – Expert System Shell with Predictive Modeling and Deep Learning.
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Rosella
Machine Intelligence & Data Mining
