Underfitting

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover underfitting?

Diagnoses underfitting through cross-validation predictions, metrics, plots, infeasible outputs, and coefficient inspection.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Underfitting is a modeling failure in which a model is too simple to capture important patterns in the data.

Underfitting occurs when a model is too simple to capture important patterns in the data. It often appears as weak predictive performance and systematically flat predictions. section 7.2.3

Linear regression baseline

A linear regression baseline for listing prices produced an R-squared of 0.32, MAE above 12,000 Yen, and predictions that departed from the ideal-fit line with a flat trend. Some predictions were negative, although only about 0.5% were nonpositive. These results signal that the baseline misses important structure rather than providing a strong final model. section 7.2.3

Use baseline diagnostics to investigate whether the model is underfitting:

Compare cross-validation predictions with expected target values across data subsets. section 7.2.3
Inspect feature coefficients and individual case contributions before choosing a more complex model. section 7.4
Use the baseline’s limitations to guide tentative choices for nonlinearities, interactions, targets, and hyperparameters. section 7.4

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

liveBook manning.com

Read Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Grokking Machine LearningA model whose complexity is insufficient to capture the relevant structure of its dataset.
liveBook manning.com

Machine Learning System DesignA condition in which a model is too simple to capture important relationships between features and the target variable, resulting in poor performance on both training and new data.
liveBook manning.com

Generative AI in ActionUnderfitting is a modeling condition in which a model fails to learn enough structure from its training data to perform well on new examples.
liveBook manning.com

Grokking AI Algorithms, Second EditionUnderfitting is a condition in which a model is too simple or insufficiently trained to perform well even on its training data.
liveBook manning.com

Deep Learning with R, Third EditionUnderfitting is a modeling condition in which a model is too simple or insufficiently trained to capture patterns needed for good performance.
liveBook manning.com

The AI Pocket BookA failure mode where a model is too simple to capture the training pattern and performs poorly.

Compare how 7 books explain underfitting →

Related concepts

overfittingcontrasts with · Overfitting is a modeling condition in which a model fits patterns or idiosyncrasies in its training data too closely and consequently generalizes poorly to new data.
genetic algorithmaddressed by · A genetic algorithm is an evolutionary optimization method that improves a population of candidate solutions through fitness-based selection and variation, often including recombination and mutation.
bias-variance tradeoffcontrasts with · The bias-variance tradeoff is the balance between systematic error from restrictive model assumptions and error from sensitivity to variation in sampled data.
learning curvemeasured by · A learning curve is a representation of how performance or proficiency changes as experience, training progress, or training data increases.
expressive poweraddresses · Expressive power is the capacity of a model, language, or system to represent or convey a range and complexity of functions, patterns, distinctions, or ideas.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.