Area under the curve

As explained in Machine Learning Algorithms in Depth

Area under the curve is a scalar summary of the area beneath a performance curve, with larger values indicating better ranking performance in the ROC sense.

Area under the curve (AUC) summarizes ROC curve quality with one number. The ROC curve plots true positive rate against false positive rate as the classification threshold varies. Higher AUC indicates better ranking performance; the maximum is 1. section 5.2

Figure 5.4 Receiver operating characteristic (ROC) plot (left) and precision-recall plot (right)

Compare how changing the classification threshold traces ROC and precision-recall curves. section 5.2

How Machine Learning Algorithms in Depth covers it

How deep it goes

Defines AUC and its interpretation but does not derive its calculation.

Examples

Uses a figure comparing ROC and precision-recall plots.

What you can do after

Evaluate classifier ranking performance from its ROC curve summary.

How it connects

Connects AUC to ROC curves, classification thresholds, true positive rate, and false positive rate.

liveBookmanning.com

Read Machine Learning Algorithms in Depth on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Grokking Machine LearningA scalar performance metric equal to the area under a receiver operating characteristic curve.
• 

  liveBookmanning.com
  Statistics Slam DunkAUC is the area under a receiver operating characteristic curve, measuring a binary classifier's ability to rank positive cases above negative cases.
• 

  liveBookmanning.com
  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIROC AUC is the area under the receiver operating characteristic curve, measuring a binary classifier's ability to rank positive instances above negative instances.
• 

  liveBookmanning.com
  Starting Data Analytics with Generative AI and PythonA metric equal to the area under a receiver operating characteristic curve that summarizes discrimination across thresholds.
• 

  liveBookmanning.com
  Outlier Detection in PythonAUROC is the area under the receiver operating characteristic curve, measuring a scoring model's ability to rank positive cases above negative cases across classification thresholds.
• 

  liveBookmanning.com
  Statistics Every Programmer NeedsThe area under the ROC curve is a measure of a binary classifier's ability to discriminate between positive and negative classes across classification thresholds.
• 

  liveBookmanning.com
  Machine Learning System DesignThe area under the receiver operating characteristic curve, summarizing ranking performance across classification thresholds.
• 

  liveBookmanning.com
  Grokking StatisticsThe area under the curve is the area beneath a receiver operating characteristic or precision-recall curve, summarizing performance across thresholds.
• 

  liveBookmanning.com
  Causal Inference for Data ScienceThe area under the ROC curve is a threshold-independent measure of binary classification performance based on the receiver operating characteristic curve.
• 

  liveBookmanning.com
  Graph Neural Networks in ActionThe area under a receiver operating characteristic curve, measuring a classifier's ability to rank positive examples above negative examples across thresholds.
• 

  liveBookmanning.com
  Financial AI in PracticeThe area under a receiver operating characteristic curve, summarizing a classifier’s ability to distinguish between classes across thresholds.

Compare how 12 books explain area under the curve →

Related concepts

• classifiermeasures · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• receiver operating characteristicmeasures · A receiver operating characteristic curve plots a binary classifier’s true-positive rate against its false-positive rate as the decision threshold varies.
• cluster-based local outlier factormeasures · Cluster-based local outlier factor is an outlier detection algorithm that scores observations using cluster membership, cluster size, and distance to relevant clusters.
• precision-recall curvemeasures · A precision-recall curve is a plot of a classifier’s precision against recall as its decision threshold varies.
• outlier detectionmeasures · Outlier detection is the process of identifying observations that substantially deviate from an expected or prevailing pattern in data.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.