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What is Overfitting

Chapter 11
Handbook of Research on Cyber Crime and Information Privacy
A condition when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
Published in Chapter:
Detection and Prediction of Spam Emails Using Machine Learning Models
Salma P. Z (NSS College of Engineering, Kerala, India) and Maya Mohan (NSS College of Engineering, Kerala, India)
Copyright: © 2021 |Pages: 18
DOI: 10.4018/978-1-7998-5728-0.ch011
Abstract
One of today's important means of communication is email. The extensive use of email for communication has led to many problems. Spam emails being the most crucial among them. It is one the major issues in today's internet world. Spam emails contain mostly advertisements and offensive content, which are often sent without the recipient's request and are generally annoying, time consuming, and wasting space on the communication media's resources. It creates inconveniences and financial loss to the recipients. Hence, there is always the need to filter the spam emails and separate them from the legitimate emails. There are a lot of content-based machine learning techniques that have proven to be effective in detecting and filtering spam emails. Due to a large increase in email spamming, the emails are studied and classified as spam or not spam. In this chapter, three machine learning models, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BLSTM), are used classify the emails as spam and benign.
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Chapter 3
Understanding Convolutional Neural Network With TensorFlow: CNN
An unfavorable machine learning characteristic happens when a model provides correct predictions for training examples but not for new data. Researchers first build the model on a collection of available information whenever data analysts make predictions using machine learning algorithms. Depending on this knowledge, the algorithm then attempts to predict results for additional data types. An overfit model might provide erroneous forecasts and need to function more effectively with all new data sources.
Published in Chapter: Understanding Convolutional Neural Network With TensorFlow: CNN; From: Machine Learning Algorithms Using Scikit and TensorFlow Environments
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Chapter 1
Obtaining Deep Learning Models for Automatic Classification of Leukocytes
Analysis that is too close to a specific dataset, that tends to fail to predict future observations or to fit additional data.
Published in Chapter: Obtaining Deep Learning Models for Automatic Classification of Leukocytes; From: Machine Learning and Deep Learning in Real-Time Applications
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Chapter 5
Applying Machine Learning to Online Data?: Beware! Computational Social Science Requires Care
Corresponds to the event when a machine learning model memorizes the training set data instead of learning the patterns present within it for accurate generalization. When a model overfits data, the model can return high within-dataset performance while it fails to generalize to other data.
Published in Chapter: Applying Machine Learning to Online Data?: Beware! Computational Social Science Requires Care; From: Opportunities and Challenges for Computational Social Science Methods
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Chapter 159
Learning From Imbalanced Data
A modeling error that occurs when a function is too closely fit to a limited set of data point is known as Overfitting.
Published in Chapter: Learning From Imbalanced Data; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 13
Plant Disease Classification Using Deep Learning Techniques
Overfitting refers to a situation where a machine learning model is excessively complex and performs well on training data, but poorly on new, unseen data.
Published in Chapter: Plant Disease Classification Using Deep Learning Techniques; From: Effective AI, Blockchain, and E-Governance Applications for Knowledge Discovery and Management
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Chapter 237
Exploiting the Strategic Potential of Data Mining
A condition that occurs when there are too many parameters in a model. In such cases, the model learns the idiosyncrasies of the test data set. This can happen in models such as regression, time series analysis, and neural networks
Published in Chapter: Exploiting the Strategic Potential of Data Mining; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 3
Medical Image Classification
The classifier accuracy would be extra ordinary when the test data and the training data are overlapping. But when the model is applied to a new data it will fail to show acceptable accuracy. This condition is called as overfitting.
Published in Chapter: Medical Image Classification; From: Medical Image Processing for Improved Clinical Diagnosis
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Chapter 91
Incremental Neural Network Training for Medical Diagnosis
Refers to fitting a model (e.g., neural network) with too many samples or parameters.
Published in Chapter: Incremental Neural Network Training for Medical Diagnosis; From: Encyclopedia of Healthcare Information Systems
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Chapter 8
Development, Monitoring, and Management Approaches of Machine Learning Implementations for the Effective Delivery of Government Services
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Chapter 5
Genetic Programming
Genetic programming evolves individuals over a training set, hopefully representative of the function to be approximated. However, the evolution driving force is usually so strong that individuals will develop specific code to match the training set as well as possible. An “overfit” solution will have a very good fitness on the training set, but will perform poorly on real data.
Published in Chapter: Genetic Programming; From: Handbook of Research on Nature-Inspired Computing for Economics and Management
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Chapter 73
Efficient High Dimensional Data Classification
On applying a learning algorithm to a smaller training data, the model developed memorizes data and cannot predict well on new unseen data.
Published in Chapter: Efficient High Dimensional Data Classification; From: Encyclopedia of Business Analytics and Optimization
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Chapter 20
Application to Bankruptcy Prediction in Banks
Fitting a model to best match the available data while loosing the capability of describing the general behaviour.
Published in Chapter: Application to Bankruptcy Prediction in Banks; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 33
Decision Trees
Fitting a statistical model that has too many parameters.
Published in Chapter: Decision Trees; From: Encyclopedia of Information Technology Curriculum Integration
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Chapter 186
Employee Classification in Reward Allocation Using ML Algorithms
In machine learning, the fitting of corresponds too closely to the training data set and may therefore fail to fit validation and testing data. An overfitted model is a statistical model that contains more parameters than can be justified by the sample data and therefore fails to predict out-of-sample data reliably.
Published in Chapter: Employee Classification in Reward Allocation Using ML Algorithms; From: Encyclopedia of Data Science and Machine Learning
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Chapter 25
Counting the Hidden Defects in Software Documents
Learning a complicated function that matches the training data closely but fails to recognize the underlying process that generates the data. As a result of overfitting, the model performs poor on new input. Overfitting occurs when the training patterns are sparse in input space and/or the trained networks are too complex.
Published in Chapter: Counting the Hidden Defects in Software Documents; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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