What range of values is valid for the quantile?

The quantile must be a decimal value between 0 and 1.

Does MODEL_QUANTILE() require historical data?

Yes, sufficient historical data is needed to build a reliable statistical model.

Is MODEL_QUANTILE() useful for predictive analysis?

Yes, it is commonly used for predictive distribution analysis and scenario planning.

Tableau

Tableau Funtion: MODEL_QUANTILE( )

Q: What does MODEL_QUANTILE() do in Tableau?

MODEL_QUANTILE() returns a predicted value at a specified quantile based on a statistical model.

Q: How is MODEL_QUANTILE() different from QUANTILE()?

MODEL_QUANTILE() uses a predictive model, while QUANTILE() calculates directly from raw data.

Tableau Function: MODEL_QUANTILE( )

Category: Table Calculation Functions

Purpose

The MODEL_QUANTILE() function in Tableau is a predictive analytics function that returns the predicted value at a specified quantile based on a statistical model fitted to the data. It is used to understand distribution cut points (such as quartiles, deciles, or other quantiles) derived from modeled behavior rather than raw data.

In simple terms, MODEL_QUANTILE() answers:
“According to the model, what value corresponds to this quantile?”

Type of Calculations

Predictive modeling calculations
Statistical distribution analysis
Quantile estimation from a fitted model
Model-based threshold calculations

Unlike standard percentile or quantile functions, MODEL_QUANTILE() relies on modeled distributions, not direct aggregation of observed values.

Practical Use Cases

Identifying modeled quartiles or deciles
Risk and probability threshold analysis
Understanding distribution spread in forecasts
Scenario planning and predictive benchmarking
Defining statistically informed cutoffs for KPIs


MODEL_QUANTILE(expression, quantile)

Parameter	Type	Description
expression	Measure (column)	The continuous numeric measure used to build the predictive model. Must be aggregated in the view.
quantile	Scalar (decimal)	A value between `0` and `1` indicating the desired quantile (e.g., `0.25`, `0.5`, `0.75`).

How It Works?

Mathematical / Logical Principle

MODEL_QUANTILE() fits a statistical model to the input measure and then computes the value at the requested quantile of that modeled distribution.

Conceptually:

MODEL_QUANTILE(X, q) = value such that P(X ≤ value) = q

Where:

X is the modeled measure
q is the quantile (0–1)

This approach smooths variability and supports more stable estimates compared to raw quantile calculations.

What Does It Return?

Data Type: Numeric (same data type as the expression)
Meaning:
- Returns the predicted value at the specified quantile of the modeled distribution
- Returns NULL if the model cannot be computed due to insufficient or invalid data

When Should We Use It?

Use MODEL_QUANTILE() when you need to:

Analyze distribution cut points using predictive models
Replace raw quantiles with statistically smoothed estimates
Understand forecasted data ranges
Perform probability-based segmentation
Support advanced analytics and decision modeling

Basic Usage

Calculate the modeled median


MODEL_QUANTILE(SUM([Sales]), 0.5)

Returns the modeled median value

Column Usage

Calculate the first and third quartiles


MODEL_QUANTILE(AVG([Profit]), 0.25)
MODEL_QUANTILE(AVG([Profit]), 0.75)

Useful for modeled IQR (interquartile range) analysis

Advanced Usage

Flag values above the modeled upper quartile


IF SUM([Sales]) &gt; MODEL_QUANTILE(SUM([Sales]), 0.75)
THEN "Above Typical Range"
ELSE "Within Typical Range"
END

Identifies values exceeding expected distribution levels

Parameter-driven quantile selection


MODEL_QUANTILE(SUM([Sales]), [Selected Quantile])

Enables interactive analytics using user-defined quantiles

Tips and Tricks

Use quantile values strictly between 0 and 1
Ensure enough historical data for reliable modeling
Combine with parameters for exploratory analysis
Results depend on the underlying data distribution
Not equivalent to raw PERCENTILE() or QUANTILE() functions

Related Functions You Might Need

Functions commonly used alongside or as alternatives to MODEL_QUANTILE():

MODEL_PERCENTILE()
MODEL_MEDIAN()
MODEL_AVERAGE()
MODEL_MAX()
MODEL_MIN()
PERCENTILE()
QUANTILE()

We’ve got plenty of resources to help you master Tableau functions. For more details, check out the official Tableau documentation. Or, if you’re ready for more practice, let’s dive into related functions and build your Tableau skills further!

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1. What does MODEL_QUANTILE() do in Tableau?

It returns a predicted value at a specified quantile based on a statistical model.

2. How is MODEL_QUANTILE() different from QUANTILE()?

MODEL_QUANTILE() uses a predictive model, while QUANTILE() calculates directly from observed data.

3. What values are valid for the quantile parameter?

Any decimal value between 0 and 1.

4. Does MODEL_QUANTILE() require a lot of data?

Larger datasets typically produce more reliable models, but it can work with smaller sets.

5. Is MODEL_QUANTILE() used for forecasting?

Yes, it is often used to analyze forecast distributions and probability thresholds.

Tableau Funtion: MODEL_PERCENTILE( )

Tableau Funtion: PREVIOUS_VALUE( )