kl_divergence#
- cuml.metrics.kl_divergence(P, Q, convert_dtype='deprecated')[source]#
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:
- PDense array of probabilities corresponding to distribution P
shape = (n_samples, 1) Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device ndarray, cuda array interface compliant array like CuPy.
- QDense array of probabilities corresponding to distribution Q
shape = (n_samples, 1) Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device ndarray, cuda array interface compliant array like CuPy.
- convert_dtypebool, default=”deprecated”
Deprecated since version 26.08:
convert_dtypewas deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.
- Returns:
- float
The KL Divergence value