Volume Predictive Modeling Techniques International On Actuarial Science

Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques (International Series on Actuarial Science)

by Edward W. Frees

4.2 out of 5

Language	:	English
File size	:	36741 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	967 pages

FREE

Volume Predictive Modeling Techniques International On Actuarial Science is a comprehensive guide to predictive modeling techniques used in actuarial science. The book covers a wide range of topics, including data preparation, model selection, model evaluation, and deployment. It is written by a team of experts in the field, and it is the ideal resource for anyone who wants to learn more about predictive modeling in actuarial science.

Table of Contents

• to Predictive Modeling
• Data Preparation
• Model Selection
• Model Evaluation
• Deployment

to Predictive Modeling

Predictive modeling is a statistical technique that uses data to predict future events. It is used in a wide variety of applications, including actuarial science, finance, and marketing. In actuarial science, predictive modeling is used to predict the likelihood of events such as death, disability, and retirement. This information is used to set insurance premiums and to develop financial products.

Data Preparation

The first step in predictive modeling is data preparation. This involves cleaning the data, removing outliers, and transforming the data into a format that is suitable for modeling. The data preparation process can be time-consuming, but it is essential to ensure that the data is accurate and reliable.

Model Selection

Once the data has been prepared, the next step is to select a predictive model. There are a variety of different predictive models available, each with its own strengths and weaknesses. The best model for a particular application will depend on the data and the desired outcome.

Model Evaluation

Once a predictive model has been selected, it is important to evaluate its performance. This involves using a holdout sample of data to test the model's accuracy. The model's performance can be evaluated using a variety of metrics, such as the mean absolute error and the root mean squared error.

Deployment

Once a predictive model has been evaluated and found to be satisfactory, it can be deployed into production. This involves making the model available to users so that they can use it to make predictions. The deployment process can be complex, but it is essential to ensure that the model is used correctly and that the results are reliable.

Volume Predictive Modeling Techniques International On Actuarial Science is a comprehensive guide to predictive modeling techniques used in actuarial science. The book covers a wide range of topics, including data preparation, model selection, model evaluation, and deployment. It is written by a team of experts in the field, and it is the ideal resource for anyone who wants to learn more about predictive modeling in actuarial science.

Free Download your copy today!

Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques (International Series on Actuarial Science)

by Edward W. Frees

4.2 out of 5

Language	:	English
File size	:	36741 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	967 pages

FREE