Use the trained decision tree to return the class probabilities for the examples in `X`. Parameters ---------- X : :py:class:`ndarray ` of shape `(N, M)` The training data of `N` examples, each with `M` features Returns
(self, X)
| 102 | return np.array([self._traverse(x, self.root) for x in X]) |
| 103 | |
| 104 | def predict_class_probs(self, X): |
| 105 | """ |
| 106 | Use the trained decision tree to return the class probabilities for the |
| 107 | examples in `X`. |
| 108 | |
| 109 | Parameters |
| 110 | ---------- |
| 111 | X : :py:class:`ndarray <numpy.ndarray>` of shape `(N, M)` |
| 112 | The training data of `N` examples, each with `M` features |
| 113 | |
| 114 | Returns |
| 115 | ------- |
| 116 | preds : :py:class:`ndarray <numpy.ndarray>` of shape `(N, n_classes)` |
| 117 | The class probabilities predicted for each example in `X`. |
| 118 | """ |
| 119 | assert self.classifier, "`predict_class_probs` undefined for classifier = False" |
| 120 | return np.array([self._traverse(x, self.root, prob=True) for x in X]) |
| 121 | |
| 122 | def _grow(self, X, Y, cur_depth=0): |
| 123 | # if all labels are the same, return a leaf |