This article describes the analytical technique of multilayer perceptrons for classification.
What is a Multilayer Perceptron Classifier?
Multilayer perceptron (MLP) is a technique of feed-forward artificial neural networks using a back propagation learning method to classify the target variable used for supervised learning.
MLP’s can be applied to complex non-linear problems, and it also works well with large input data with a relatively faster performance. The algorithm tends to achieve the same accuracy ratio even with smaller data.
Let’s look at an example of Multilayer perceptron analysis, involving the potential for opportunity results, based on factors like Revenue, Total days(qualified), Total days(closing), Ratio days, Sales Stage.
How is a Multilayer Perceptron Classifier Used in Analysis?
Let’s look at two use cases where a Multilayer Perceptron Classifier might be applied and how it would be useful to the organization.
Business Use Case 1
Business Problem: Predict employee attrition.
Identifying the important factors that lead to employee attrition.
- Monthly Income
- Total Working Years
- Stock Option Level
- Relationship Satisfaction
The predictive model will help us identify various factors that affect the resignation or retirement decisions made by the employee. This will help the company identify the criteria it needs to work on to retain employees in the company.
Business Use Case 2
Business Problem: Predicting medication type needed for patients in a hospital.
Identifying the right type of medication/treatment for various patients admitted in the hospital.
- Target (Drug, Solo Insulin)
- Time Spent in Hospital
- Number of Medications
- Number of Procedures
- Patient’s Weight
- Medical Specialty Ward
Filtering through the most important factors of a patient’s diagnosis to help choose the most appropriate type of medication (Drug, Solo Insulin) for the patient.
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