Numerical Target Drift

TL;DR: The report explores the changes in the numerical target function (prediction).

  • Performs a suitable statistical test to compare target (prediction) distribution

  • Calculates the correlations between the feature and the target (prediction)

  • Plots the relations between each individual feature and the target (prediction)


The Target Drift report helps detect and explore changes in the target function and/or model predictions.

The Numerical Target Drift report is suitable for problem statements with the numerical target function: regression, probabilistic classification, ranking, etc.


To run this report, you need to have input features, and target and/or prediction columns available.

You will need two datasets. The reference dataset serves as a benchmark. We analyze the change by comparing the current production data to the reference data.

You can potentially choose any two datasets for comparison. But keep in mind that only the reference dataset will be used as a basis for comparison.

How it works

We estimate the drift for the target (actual values) and predictions in the same manner. If both columns are passed to the dashboard, we build two sets of plots.

If only one of them (either target or predictions) is provided, we build one set of plots. If neither target nor predictions column is available, you will get an error.

To estimate the numerical target (prediction) drift, we compare the distribution of the target (prediction) in the two datasets. We use the two-sample Kolmogorov-Smirnov test at a 0.95 confidence level to detect if the distribution has changed significantly.

We also calculate the Pearson correlation between the target (prediction) and each individual feature in the two datasets to detect a change in the relationship.

How it looks

The report includes 4 components. All plots are interactive.

1. Target (Prediction) Drift

The report first shows the comparison of target (prediction) distributions in the current and reference dataset. The result of the statistical test and P-value are displayed in the title.

2. Target (Prediction) Correlations

The report shows the correlations between individual features and the target (prediction) in the current and reference dataset. It helps detects shifts in the relationship.

3. Target (Prediction) Values

The report visualizes the target (prediction) values by index or time (if thedatetime column is available or defined in the column_mapping dictionary). This plot helps explore the target behavior and compare it between the datasets.

4. Target (Prediction) Behavior By Feature

Finally, we generate an interactive table with the visualizations of dependencies between the target and each feature.

If you click on any feature in the table, you get an overview of its behavior.

The plot shows how feature values relate to the target (prediction) values and if there are differences between the datasets. It helps explore if they can explain the target (prediction) shift.

We recommend paying attention to the behavior of the most important features since significant changes might confuse the model and cause higher errors.

For example, in a Boston house pricing dataset, we can see a new segment with values of TAX above 600 but the low value of the target (house price).

When to use the report

Here are our suggestions on when to use it—best combined with the Data Drift report. 1. Before model retraining. Before feeding fresh data into the model, you might want to verify whether it even makes sense. 2. When you are debugging the model decay. If you observe a drop in performance, this report can help see what has changed. 3. When you are flying blind, and no ground truth is available. If you do not have immediate feedback, you can use this report to explore the changes in the model output and the relationship between the features and prediction. This can help anticipate data and concept drift.

JSON Profile

If you choose to generate a JSON profile, it will contain the following information:

"num_target_drift": {
"name": "num_target_drift",
"datetime": "datetime",
"data": {
"utility_columns": {
"date": null,
"id": null,
"target": "target",
"prediction": null
"cat_feature_names": [],
"num_feature_names": [],
"metrics": {
"target_name": "target",
"target_type": "num",
"target_drift": p_value,
"target_correlations": {
"reference": {
"feature_name": corr_coefficient
"current": {
"feature_name": corr_coefficient
"timestamp": "timestamp"


  • Browse our examples for sample Jupyter notebooks.

You can also read the initial release blog.