# Brier score

The Brier score is a proper score function that measures the accuracy of probabilistic predictions. It is applicable to tasks in which predictions must assign probabilities to a set of mutually exclusive discrete outcomes. The set of possible outcomes can be either binary or categorical in nature, and the probabilities assigned to this set of outcomes must sum to one (where each individual probability is in the range of 0 to 1). It was proposed by Glenn W. Brier in 1950.[1]

The Brier score can be thought of as either a measure of the "calibration" of a set of probabilistic predictions, or as a "cost function". More precisely, across all items ${\displaystyle i\in {1...N}}$ in a set N predictions, the Brier score measures the mean squared difference between:

• The predicted probability assigned to the possible outcomes for item i
• The actual outcome ${\displaystyle o_{i}}$

Therefore, the lower the Brier score is for a set of predictions, the better the predictions are calibrated. Note that the Brier score, in its most common formulation, takes on a value between zero and one, since this is the largest possible difference between a predicted probability (which must be between zero and one) and the actual outcome (which can take on values of only 0 or 1). In the original (1950) formulation of the Brier score, the range is double, from zero to two.

The Brier score is appropriate for binary and categorical outcomes that can be structured as true or false, but is inappropriate for ordinal variables which can take on three or more values (this is because the Brier score assumes that all possible outcomes are equivalently "distant" from one another).

## DefinitionEdit

The most common formulation of the Brier score is

${\displaystyle BS={\frac {1}{N}}\sum \limits _{t=1}^{N}(f_{t}-o_{t})^{2}\,\!}$

in which ${\displaystyle f_{t}}$  is the probability that was forecast, ${\displaystyle o_{t}}$  the actual outcome of the event at instance ${\displaystyle t}$  (${\displaystyle 0}$  if it does not happen and ${\displaystyle 1}$  if it does happen) and ${\displaystyle N}$  is the number of forecasting instances. In effect, it is the mean squared error of the forecast. This formulation is mostly used for binary events (for example "rain" or "no rain"). The above equation is a proper scoring rule only for binary events; if a multi-category forecast is to be evaluated, then the original definition given by Brier below should be used.

### ExampleEdit

Suppose that one is forecasting the probability ${\displaystyle P}$  that it will rain on a given day. Then the Brier score is calculated as follows:

• If the forecast is 100% (${\displaystyle P}$  = 1) and it rains, then the Brier Score is 0, the best score achievable.
• If the forecast is 100% and it does not rain, then the Brier Score is 1, the worst score achievable.
• If the forecast is 70% (${\displaystyle P}$  = 0.70) and it rains, then the Brier Score is (0.70−1)2 = 0.09.
• If the forecast is 30% (${\displaystyle P}$  = 0.30) and it rains, then the Brier Score is (0.30−1)2 = 0.49.
• If the forecast is 50% (${\displaystyle P}$  = 0.50), then the Brier score is (0.50−1)2 = (0.50−0)2 = 0.25, regardless of whether it rains.

### Original definition by BrierEdit

Although the above formulation is the most widely used, the original definition by Brier [1] is applicable to multi-category forecasts as well as it remains a proper scoring rule, while the binary form is only proper for binary events. For binary forecasts the original formulation of Brier's "probability score" has twice the value of the score currently known as the Brier score.

${\displaystyle BS={\frac {1}{N}}\sum \limits _{t=1}^{N}\sum \limits _{i=1}^{R}(f_{ti}-o_{ti})^{2}\,\!}$

In which ${\displaystyle R}$  is the number of possible classes in which the event can fall, and ${\displaystyle N}$  the overall number of instances of all classes. For the case Rain / No rain, ${\displaystyle R=2}$ , while for the forecast Cold / Normal / Warm, ${\displaystyle R=3}$ .

## DecompositionsEdit

There are several decompositions of the Brier score which provide a deeper insight on the behavior of a binary classifier.

### 3-component decompositionEdit

The Brier score can be decomposed into 3 additive components: Uncertainty, Reliability, and Resolution. (Murphy 1973)[2]

${\displaystyle BS=REL-RES+UNC}$

Each of these components can be decomposed further according to the number of possible classes in which the event can fall. Abusing the equality sign:

${\displaystyle BS={\frac {1}{N}}\sum \limits _{k=1}^{K}{n_{k}(\mathbf {f_{k}} -\mathbf {\bar {o}} _{\mathbf {k} })}^{2}-{\frac {1}{N}}\sum \limits _{k=1}^{K}{n_{k}(\mathbf {{\bar {o}}_{k}} -{\bar {\mathbf {o} }})}^{2}+\mathbf {\bar {o}} \left({1-\mathbf {\bar {o}} }\right)}$

With ${\displaystyle \textstyle N}$  being the total number of forecasts issued, ${\displaystyle \textstyle K}$  the number of unique forecasts issued, ${\displaystyle \mathbf {\bar {o}} ={\sum _{t=1}^{N}}\mathbf {o_{t}} /N}$  the observed climatological base rate for the event to occur, ${\displaystyle n_{k}}$  the number of forecasts with the same probability category and ${\displaystyle \mathbf {\overline {o}} _{\mathbf {k} }}$  the observed frequency, given forecasts of probability ${\displaystyle \mathbf {f_{k}} }$ . The bold notation is in the above formula indicates vectors, which is another way of denoting the original definition of the score and decomposing it according to the number of possible classes in which the event can fall. For example, a 70% chance of rain and an occurrence of no rain are denoted as ${\displaystyle \mathbf {f} =(0.3,0.7)}$  and ${\displaystyle \mathbf {o} =(1,0)}$  respectively. Operations like the square and multiplication on these vectors are understood to be component wise. The Brier Score is then the sum of the resulting vector on the right hand side.

#### UncertaintyEdit

The uncertainty term measures the inherent uncertainty in the outcomes of the event. For binary events, it is at a maximum when each outcome occurs 50% of the time, and is minimal (zero) if an outcome always occurs or never occurs.

#### ReliabilityEdit

The reliability term measures how close the forecast probabilities are to the true probabilities, given that forecast. Reliability is defined in the contrary direction compared to English language. If the reliability is 0, the forecast is perfectly reliable. For example, if we group all forecast instances where 80% chance of rain was forecast, we get a perfect reliability only if it rained 4 out of 5 times after such a forecast was issued.

#### ResolutionEdit

The resolution term measures how much the conditional probabilities given the different forecasts differ from the climatic average. The higher this term is the better. In the worst case, when the climatic probability is always forecast, the resolution is zero. In the best case, when the conditional probabilities are zero and one, the resolution is equal to the uncertainty.

### Two-component decompositionEdit

An alternative (and related) decomposition generates two terms instead of three.

${\displaystyle BS=CAL+REF}$
${\displaystyle BS={\frac {1}{N}}\sum \limits _{k=1}^{K}{n_{k}(\mathbf {f_{k}} -\mathbf {\bar {o}} _{\mathbf {k} })}^{2}+{\frac {1}{N}}\sum \limits _{k=1}^{K}{n_{k}(\mathbf {{\bar {o}}_{k}} (1-\mathbf {{\bar {o}}_{k}} }))}$

The first term is known as calibration (and can be used as a measure of calibration, see statistical calibration), as is equal to reliability. The second term is known as refinement, and it is an aggregation of resolution and uncertainty, and is related to the area under the ROC Curve.

The Brier Score, and the CAL + REF decomposition, can be represented graphically through the so-called Brier Curves,[3] where the expected loss is shown for each operating condition. This makes the Brier Score a measure of aggregated performance under a uniform distribution of class asymmetries.[4]

## ShortcomingsEdit

The Brier score becomes inadequate for very rare (or very frequent) events, because it does not sufficiently discriminate between small changes in forecast that are significant for rare events.[5] Wilks (2010) has found that "[Q]uite large sample sizes, i.e. n > 1000, are required for higher-skill forecasts of relatively rare events, whereas only quite modest sample sizes are needed for low-skill forecasts of common events." [6]