Future Events Performance

How do I evaluate the performance of my Future Events pipeline?

Once you’ve built a Future Events pipeline, Cortex makes it easy to explore results and learn more about your pipeline’s predictive power. In this guide, we’ll show you how to evaluate the performance of a Future Events pipeline in Cortex.

Cortex summarizes performance of any pipeline in three ways: Pipeline Quality, Performance Metrics, and Performance Graphs. The following sections describe how to interpret each of these specifically for a Future Events pipeline.

Future Events Pipeline Quality

Pipeline Quality gives you a quick sense for your pipeline’s performance without having to scrutinize any technical metrics. A pipeline’s Quality is meant to serve as a rough guide for how well its predictions match reality, but in truth what constitutes good performance depends on the difficulty of the problem – sometimes “Average” actually represents the best possible performance that can be achieved with the data at hand.

For Future Events, Pipeline Quality is determined based on AUC, a common measure of performance for binary classification machine learning. AUC is described in more detail in the Metrics section below.

Pipeline Quality AUC
Excellent >85%
Very Good 75-85%
Good 65-75%
Average 55-65%
Below Average <55%

Future Events Performance Metrics

Cortex publishes four well-known performance metrics for each Future Events pipeline. All metrics are computed on a holdout set (i.e. data not used during training) to make sure your pipeline can generalize to data it’s never seen before.

To frame these metrics in real terms, consider a Future Events pipeline which predicts each user’s probability of purchasing within the next 14 days. Note however that your Cortex account can be configured to make predictions about any type of object tied to your event data (e.g. commerce items, media content, home listings, etc.).


AUC (or AUROC, short for Area Under the Receiver Operating Characteristics curve) is one of the most commonly-used measures of classification performance. It is represented as a percentage from 0-100%. The higher your pipeline’s AUC, the better its predictive power.

AUC is derived from the ROC curve (read here for more details), but can be interpreted in a more intuitive way: if a positive and negative label are both drawn at random, what is the probability that the positive label was given a higher prediction than the negative label? In our example above, a positive label is a user who purchased within 14 days, while a negative label is a user who did not end up purchasing in the window.


Of all the users that your pipeline predicted to be in the positive class, what percentage were actually in the positive class? In terms of our example, of all the users predicted to purchase within 14 days, what percent actually did? Read here for more details.


Of all the users that were actually in the positive class, what percentage did your pipeline predict to be in the positive class? In terms of our example, of all the users who purchased within 14 days, what percent were predicted to do so? Read here for more details.


What percent of all users did your pipeline classify correctly across both the positive and negative classes? In terms of our example, what percent of all predictions were correct (for both users who were and were not predicted to purchase)? Read here for more details.

*Note: Precision, recall, and accuracy are reported at the maximum F1 score of each Future Events pipeline’s precision-recall curve (described in the Performance Graphs section below).

Future Events Performance Graphs

Propensity Cohort Analysis

The Propensity Cohort Analysis graph helps you gauge whether the performance seen during the train/test process can be replicated on “live” data. The graph is designed to answer two related questions:

  1. Did the users that your pipeline considered most likely to convert end up actually converting more often than random?
  2. Did the users that your pipeline considered least likely to convert end up actually converting less often than random?

To answer these questions, Cortex automatically generates three cohorts based on predicted conversion rates for each Future Events pipeline:

  • High Propensity: the 10% of users with the highest predicted conversion rates
  • Low Propensity: the 10% of users with the lowest predicted conversion rates
  • Control: a random 10% of all users (regardless of predicted conversion rate)

Next, Cortex tracks how many actual conversions occur within each cohort during the pipeline’s initial prediction window (i.e. the first X days after your pipeline is built, where X depends on the window specified during pipeline creation).

The more accurate your pipeline’s predictions are, the more separation you’ll see between the cumulative conversions lines for each cohort. In terms of our example defined in the Metrics section, this means that (1) the users Cortex predicted as most likely to purchase did in fact go on to purchase at a greater rate than random users over the next 14 days, while (2) the users Cortex predicted as least likely to purchase ended up purchasing at a lower rate than random users.

Precision-Recall Curve

The precision-recall curve measures the tradeoff between two important ML metrics, as the threshold for a positive class prediction changes. The more accurate your pipeline, the larger the area beneath this curve.

For a given pipeline, precision and recall are often inversely related — improving one usually means sacrificing the other. To understand why, note first that Cortex’s Future Events predictions take the form of a “conversion rate”.

user_id conversion_rate
ABC 0.934
DEF 0.817
UVW 0.0127
XYZ 0.0092


In reference to the above table:

  • This sample table is sorted in descending order of predicted conversion rates.
  • User ABC is predicted as most likely to convert (93.4%).
  • User XYZ is predicted as least likely to convert (0.9%).

Conversion rates are useful in that they tell you something about the confidence of each prediction. But precision and recall measure whether a pipeline’s predictions are right or wrong, so in order to compute these metrics we must first set a conversion rate threshold. Any user scored above this threshold is considered a positive class prediction (e.g. predicted to purchase within 14 days), and any user below this threshold is considered a negative class prediction (e.g. predicted not to purchase within 14 days).

Raising this threshold is likely to increase our pipeline’s precision but lower its recall. Imagine setting a high threshold such that only a few high-confidence users are considered positive predictions (say, those with conversion rates above 90%). Our pipeline would get a lot of these predictions “right” (i.e. have high precision), but would miss out on many other users that end up converting (i.e. have low recall). If we set a low threshold so that most predictions are considered positive (say, conversion rates above 10%), many of these users won’t end up converting (low precision), but we’d correctly capture almost all those that do convert (high recall).

The Precision-Recall Curve measures this tradeoff as we vary the conversion rate threshold from high (left side of the curve) to low (right side).

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