Applied Predictive Modeling: An Overview of Applied Predictive Modeling

By Steven Taylor

Predictive modeling uses statistics in order to predict outcomes. However, predictive modeling can be applied to future and to any other kind of unknown event, regardless of when it happened. When it comes to the applications of predictive modeling, techniques are used in various fields including algorithmic trading, uplift modeling, archaeology, health care, customer relationship management and many others. This book covers the predictive modeling process with fundamental steps of the process, data preprocessing, data splitting and crucial steps of model tuning and improving model performance. Further, the book will introduce you to the most common classification and regression techniques including logistic regression which is widely used when it comes to the finding the probability of event success or event failure. You will get to know the common predictive modeling techniques as well such as stepwise regression, polynomial regression and ridge regression which will help you when you are dealing with the data that suffers from very common multicollinearity where independent variables are highly correlated.

The text then provides fundamental steps to effective predictive modeling. In the second chapter, you will learn how to build your own predictive model with logistic regression and Python. You will find data sets as well as corresponding codes. On of the crucial predictive modeling steps is model tuning, so you will learn some common techniques used in order to improve your model performance. You will get to know how to tune the parameters commonly used to increase the overall predictive power. Predictive modeling comes with a few obstacles and challenges like class imbalance. Imbalanced classes commonly put the accuracy of the model out of business, but you will learn how to properly handle class imbalance which will significantly improve the accuracy of your model. The book is multi-purpose focused on to predictive modeling process and predictive modeling techniques, so it will be of great help for those who are interested in predictive modeling techniques and applications. So, it is the right time to simplify the analysis, boost productivity as well as save time. The book will be your companion on your journey towards highly accurate predictive models.

What you will learn in Applied Predictive Modeling:

• Most common predictive modeling techniques
• Types of regression models
• The overall predictive modeling process
• Fundamental steps to effective and highly accurate predictive modeling
• How to build predictive model with logistic regression with code listings
• How to build predictive model using Python
• How to enhance your model performance
• Parameters for increasing the overall predictive power
• How to handle class imbalance
• Common causes of poor model performance

Get this book now and learn more about Applied Predictive Modeling!

• Language: English
• Publisher: Steven Taylor
• Release date: Sep 7, 2019
• ISBN: 9781393737018

Steven Taylor

Dr. Steven Taylor is a Professor and Clinical Psychologist in the Department of Psychiatry at the University of British Columbia. For 10 years he was Associate Editor of Behavior Research and Therapy, and now is Associate Editor of the Journal of Cognitive Psychotherapy. He has published over 100 journal articles, over 35 book chapters, and 8 books on anxiety disorders and related topics. His most recent books are on the nature and treatment of hypochondriasis, which is commonly considered to be an OC spectrum disorder. He served as a consultant on the text revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR), and is a member of the scientific advisory board of the Anxiety Disorders Association of Canada. He has received early career awards from the Canadian Psychological Association, the Association for Advancement of Behavior Therapy, and the Anxiety Disorders Association of America. He is also a Fellow of the Canadian Psychological Association and the Association of Cognitive Therapy. His clinical and research interests include cognitive-behavioral treatments and mechanisms of anxiety disordres and related conditions.

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Introduction

Predictive modeling is a process of creating models. Predictive modeling further requires testing and validating that model in order to predict the probability of any outcome. When it comes to the predictive analytics software, there are a number of different modeling methods including artificial intelligence, machine learning, and statistics. These methods are used as predictive analytics software solutions in order to predict an outcome. Every model is chosen on the basis of validation, testing, and evaluation using the detection theory in order to guess the probability of an outcome in terms of a given set of various input ideas.

Models can use multiple classifiers when it comes to trying to determine the probability of a collection of data specifically when belonging to another collection. Different models prominently available on the particular modeling portfolio, in fact, allow you to obtain relevant and useful information. The model also gives you the information needed to develop different predictive models. Every derived model has its own weaknesses and strengths, so it is best suited for certain types of problems to be solved.

Every model is created by training an algorithm by using various historical data. Further, the model is saved in order to be used again, so every model is reusable. In order to analyze relevant results, but without the historical data, a reused model is applied using a previously trained algorithm. Common business rules can be easily applied to various similar data. The majority of the predictive modeling solutions has the capability to export various models’ information into a local file in PMML, or Predictive Modeling Markup Language. This standard format is used for sharing your model with other PMML applications in order to perform analysis on different similar data. Predictive modeling solutions commonly use statistics in order to predict an outcome.

The event that you want to predict is in the future. However, predictive modeling also may be applied to various unknown events as well, regardless of when those events happened. For instance, predictive models are used in order to detect criminal affairs and identify suspects after a certain crime has occurred.

In a majority of cases, the model is chosen by using detection theory in order to guess the probability of any outcome when you have a set amount of various input data. For instance, you can use an email and try to determine how likely that email is spam. In this case, models may use multiple classifiers when it comes to trying to determine that data set. Data sets in this case may be ham or spam. Predictive modeling is synonymous with the field of machine learning depending on some definitional boundaries. Predictive modeling is also referred to as a development or research context. When it comes to deploying predictive models commercially, then they are referred to as the field of predictive analytics.

Predictive modeling processes also use data mining when it comes to predicting outcomes. Every model is based on a different number of various predictors. Predictors are different variables which greatly influence every future result or outcome. Once data is gathered for different relevant predictors, statistical models are formulated. These models also may employ a complex neural network as well as a simple linear equation eminently mapped out by sophisticated software. Further, additional data becomes visible so the statistical analysis model is revised or validated. Predictive modeling techniques are commonly associated with weather forecasting and meteorology. However, predictive modeling has many other applications when it comes to business management. For instance, Bayesian spam filters are used in predictive modeling in order to identify the probability that a given message is spam. When it comes to fraud detection, predictive modeling is widely used in order to identify different outliers