If a string is passed it must match a valid predefined metric. The metric to use to compute distances in high dimensional space. metric: string or function (optional, default вЂeuclideanвЂ™) Provide easy visualization, but can reasonably be set to any n_components: int (optional, default 2) The dimension of the space to embed into. Values result in more local data being preserved. Result in more global views of the manifold, while smaller Sample points) used for manifold approximation. n_neighbors: float (optional, default 15) The size of local neighborhood (in terms of number of neighboring

Uniform Manifold Approximation and ProjectionДЇinds a low dimensional embedding of the data that approximatesДЄn underlying manifold.

UMAP ( n_neighbors=15, n_components=2, metric='euclidean', metric_kwds=None, output_metric='euclidean', output_metric_kwds=None, n_epochs=None, learning_rate=1.0, init='spectral', min_dist=0.1, spread=1.0, low_memory=True, n_jobs=-1, set_op_mix_ratio=1.0, local_connectivity=1.0, repulsion_strength=1.0, negative_sample_rate=5, transform_queue_size=4.0, a=None, b=None, random_state=None, angular_rp_forest=False, target_n_neighbors=-1, target_metric='categorical', target_metric_kwds=None, target_weight=0.5, transform_seed=42, transform_mode='embedding', force_approximation_algorithm=False, verbose=False, tqdm_kwds=None, unique=False, densmap=False, dens_lambda=2.0, dens_frac=0.3, dens_var_shift=0.1, output_dens=False, disconnection_distance=None, precomputed_knn=(None, None, None) ) В¶

The forest mod api update