# mean_squared_log_error

### cuml.metrics.mean_squared_log_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average', squared=True)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/metrics/regression.py#L389)

Mean squared log error regression loss

Be careful when using this metric with float32 inputs as the result can be
slightly incorrect because of floating point precision if the input is
large enough. float64 will have lower numerical error.

* **Parameters:**
  **y_true**
  : or (n_samples, n_outputs)
    Ground truth (correct) target values.

  **y_pred**
  : or (n_samples, n_outputs)
    Estimated target values.

  **sample_weight**
  : Sample weights.

  **multioutput**
  : or array-like of shape (n_outputs)
    Defines aggregating of multiple output values.
    Array-like value defines weights used to average errors.
    ‘raw_values’ :
    Returns a full set of errors in case of multioutput input.
    ‘uniform_average’ :
    Errors of all outputs are averaged with uniform weight.

  **squared**
  : If True returns MSE value, if False returns RMSE value.
* **Returns:**
  **loss**
  : A non-negative floating point value (the best value is 0.0), or an
    array of floating point values, one for each individual target.

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