# kl_divergence

### cuml.metrics.kl_divergence(P, Q)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/metrics/kl_divergence.pyx#L27)

Calculates the “Kullback-Leibler” Divergence
The KL divergence tells us how well the probability distribution Q
approximates the probability distribution P
It is often also used as a ‘distance metric’ between two probability
distributions (not symmetric)

* **Parameters:**
  **P**
  : shape = (n_samples, 1)
    Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device
    ndarray, cuda array interface compliant array like CuPy.

  **Q**
  : shape = (n_samples, 1)
    Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device
    ndarray, cuda array interface compliant array like CuPy.
* **Returns:**
  float
  : The KL Divergence value

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