evalml.model_understanding.graph_t_sne¶
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evalml.model_understanding.graph_t_sne(X, n_components=2, perplexity=30.0, learning_rate=200.0, metric='euclidean', marker_line_width=2, marker_size=7, **kwargs)[source]¶ Plot high dimensional data into lower dimensional space using t-SNE .
- Parameters
X (np.ndarray, pd.DataFrame) – Data to be transformed. Must be numeric.
n_components (int, optional) – Dimension of the embedded space.
perplexity (float, optional) – Related to the number of nearest neighbors that is used in other manifold learning
Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50. (algorithms.) –
learning_rate (float, optional) – Usually in the range [10.0, 1000.0]. If the cost function gets stuck in a bad
minimum (local) –
the learning rate may help. (increasing) –
metric (str, optional) – The metric to use when calculating distance between instances in a feature array.
marker_line_width (int, optional) – Determines the line width of the marker boundary.
marker_size (int, optional) – Determines the size of the marker.
- Returns
plotly.Figure representing the transformed data