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sklearn.metrics.max_error() function in Python
• Last Updated : 01 Nov, 2020

The max_error() function computes the maximum residual error. A metric that captures the worst-case error between the predicted value and the true value. This function compares each element (index wise) of both lists, tuples or data frames and returns the count of unmatched elements.

Syntax: sklearn.metrics.max_error(y_true, y_pred)

Parameters:

y_true: It accepts the true (correct) target values.

y_pred: It accepts the estimate target value.

Returns:

max_error:<float>: A positive floating-point value.

Example 1:

## Python3

 `# Import required module``from` `sklearn.metrics ``import` `max_error`` ` `# Assign data``y_true ``=` `[``6``, ``2``, ``5``, ``1``]``y_pred ``=` `[``4``, ``2``, ``7``, ``1``]`` ` `# Compute max_error``print``(max_error(y_true, y_pred))`

Output :

```2
```

In the above example, the elements in lists y_true and y_pred are different at index 0 and 2 only. Hence, 2 is the max_error.

Example 2:

## Python3

 `# Import required module``from` `sklearn.metrics ``import` `max_error`` ` `# Assign data``y_true ``=` `[``3.13``,``'GFG'``,``56``,``57667``]``y_pred ``=` `[``'Geeks'``,``'for'``,``'Geeks'``,``3000``]`` ` `# Compute max_error``print``(max_error(y_true, y_pred))`

Output :

UFuncTypeError: ufunc ‘subtract’ did not contain a loop with signature
matching types (dtype(‘<U32’), dtype(‘<U32’)) -> dtype(‘<U32’)

In order to use max_error(), the elements of both the lists, tuple, data frame etc. should be of similar type.

Example 3:

## Python3

 `# Import required module``from` `sklearn.metrics ``import` `max_error`` ` `# Assign data``List` `=` `[``1``, ``2``, ``3``, ``4``, ``5``, ``6``, ``7``, ``8``, ``9``]``y_true ``=` `List``y_pred ``=` `List``[::``-``1``]`` ` `# Compute max_error``print``(max_error(y_true, y_pred))`

Output :

```8
```

Here, there is only 1 matched element.

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