The model demonstrates high performance on the training data (90% recall) but significantly lower performance on the unseen testing data (40% recall). This large discrepancy is a classic symptom of overfitting. Overfitting occurs when a machine learning model learns the training data too well, including its noise and specific patterns, to the point where it negatively impacts the model's ability to generalize to new, unseen data. The model has effectively "memorized" the training set instead of learning the underlying, generalizable trends.

1. Amazon SageMaker Developer Guide: In the section on model tuning

it is stated that a sign of overfitting is when the model performs well on the training set but poorly on the validation set. "If the training accuracy is high and the validation accuracy is low

the model is likely overfitting to the training dataset."

Source: Amazon SageMaker Developer Guide

"Monitor and Analyze Training Jobs Using Metrics

" section on "Look for overfitting or underfitting".

2. Stanford University

CS229 Machine Learning Course Notes: The course materials define overfitting as a state where a model has a low training error but a high generalization (test) error. This discrepancy indicates the model has failed to capture the true underlying pattern and instead modeled the noise in the training data.

Source: Ng

A. (2023). CS229 Machine Learning Course Notes. Stanford University. Part V

"Learning Theory

" Section on Bias/variance tradeoff.

3. MIT OpenCourseWare

Introduction to Machine Learning: Lecture notes explain that overfitting happens when a model is too complex for the amount of training data available. This results in a model that fits the training data points almost perfectly but fails to generalize to new data points

leading to a large gap between training and test performance.

Source: MIT OpenCourseWare. (2020). 6.036 Introduction to Machine Learning

Fall 2020. Lecture 9 Notes: "Regularization

" Section 9.1.