Importance score

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

Feature importance is a measure of how strongly an input feature contributes to a model's predictions or performance.

An importance score estimates how strongly an input feature affects a model’s overall behavior or performance. It helps make model decisions more transparent, especially for business stakeholders and regulated applications. section 3.2 section 3.2.3

Airbnb price prediction

For an XGBoost model predicting whether an Airbnb listing exceeds the median price, built-in gain scores rank room_type highest, neighbourhood_group a distant second, and the remaining features much lower. Gain measures the average improvement from splits that use a feature. section 3.2.2

Figure 3.14 Feature importance for the Airbnb NYC problem according to XGBoost

Use the feature-importance chart to compare the relative influence of the Airbnb input features at a glance. section 3.2.2

XGBoost exposes feature importance through its built-in API, including model.feature_importances_ and a bar-chart workflow. Keras does not provide an equivalent built-in facility, making importance assessment less direct. section 3.2.2 section 3.2.3

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

How deep it goes

Defines importance and demonstrates XGBoost gain scores, but stops short of detailing alternative importance methods or Keras implementation.

Examples

Uses a house-market dataset and an Airbnb NYC price-classification model, including gain values, feature rankings, and a bar chart.

What it assumes

Assumes a trained predictive model with named input features and introduces importance as a transparency measure.

What you can do after

Apply it via XGBoost’s built-in API to rank features and visualize their relative importance.

How it connects

Connects feature importance with model transparency, explainability, gradient boosting, deep learning, XGBoost, and Keras.

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Also explained in

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  Outlier Detection in PythonFeature importance is a quantitative indication of how much an input feature contributes to a model’s prediction, score, or decision.
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  Statistics Every Programmer NeedsFeature importance is a measure of how strongly each input feature contributes to a model's predictions, often based on its effect on split quality.
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  Financial AI in PracticeFeature importance is a measure of the contribution or influence of an input feature on a model’s predictions.

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Related concepts

• random forestused by · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.
• permutation feature importanceincludes · Permutation feature importance measures a feature’s importance to a model by randomly permuting its values and assessing the resulting change in predictive performance.
• tree-based methoduses · A tree-based method is a predictive or decision-making technique that represents its rules or decision structure using one or more trees.
• SHAPmeasured by · SHAP is a feature-attribution method that explains model outputs by assigning input features contributions based on Shapley values.

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.