evalml.model_understanding.graph_t_sne

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, ww.DataTable) – 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

evalml.model_understanding.graph_partial_dependence evalml.model_understanding.prediction_explanations.explain_prediction