A predictive model is simply an equation used to predict something. A predictive model is a statistical model that is designed to predict the likelihood of target occurrences given established variables or factors. There are other factors to think about when deciding which type of predictive model is optimal for a given application. Thus, the main objective of developing predictive model is to produce a useful ONE!
Predictive analytics is data mining exercise that uses your data to build a predictive model specialized for any objectives. If the correct data are available, we can use them (along with some sort of statistical software package) to develop a predictive model that will tell us the answer to each of the above questions. After you know what you're trying to predict, you then must make sure the correct data are available for model development. If you don't have appropriate initial data from which to build the model, a sample test may have to be run to collect some.
Technically, model development can be derived based on pattern characteristics such as neural network and first layer mathematical equation . The technique automatically determines a mathematical equation that minimizes some measure of the error between the prediction from the regression model and the actual data. The most useful predictive model is the one which be able to explained a new set of data NOT the trained or modeled data - this is a wrong concept.
There are many kinds of models, such as linear regression, multiple regression, mathematic model from predictors behavior, design of experiment , neural network etc. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematic. The process of learning to distinguish and discriminate between different input patterns using a supervised training algorithm is a key to generate a good predictive model. Classification is another technique of determining that a record belongs to a group. However, a simple tool such as linear regression can not be underestimated. Most of time, the problem can be explained by just a simple linear relationship.
To get a better picture of predictive model accuracy, we should apply probability factor such as Monte Carlo simulation. This, will help us infer the predicted value with a greater confidence level.
In order to decode practical problem, we got to develop predictive model ,then we can apply statistical solution to guide us making a right decision. This is the power of NUMBERS.
By: Bard
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