[MRG] DOC Numpy doc validations to LogisticRegression
#15445
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LogisticRegressionLogisticRegression
rth
reviewed
Nov 2, 2019
glemaitre
reviewed
Nov 2, 2019
sklearn/base.py
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| Parameters | ||
| ---------- | ||
| **params : dict | ||
| Estimator parameters. |
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You should spaces (4 spaces) instead of a tabulation
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You still have 4 spaces too much :)
sklearn/base.py
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| Parameters | ||
| ---------- | ||
| deep : boolean, optional | ||
| deep : bool, optional |
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| deep : bool, optional | |
| deep : bool, default=True |
sklearn/base.py
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| Returns | ||
| ------- | ||
| self | ||
| self: object |
sklearn/base.py
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| Returns | ||
| ------- | ||
| self | ||
| self: object | ||
| Estimator instance. |
sklearn/linear_model/_base.py
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| @@ -210,7 +210,8 @@ def _decision_function(self, X): | |||
| dense_output=True) + self.intercept_ | |||
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| def predict(self, X): | |||
| """Predict using the linear model | |||
| """ | |||
| Predict using the linear model | |||
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| Predict using the linear model | |
| Predict using the linear model. |
sklearn/linear_model/_logistic.py
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| @@ -1622,6 +1623,8 @@ def predict_proba(self, X): | |||
| Parameters | |||
| ---------- | |||
| X : array-like of shape (n_samples, n_features) | |||
| Scoring vector, where n_samples is the number of samples and | |||
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| Scoring vector, where n_samples is the number of samples and | |
| Vector to be scored, where `n_samples` is the number of samples and |
sklearn/linear_model/_logistic.py
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| @@ -1622,6 +1623,8 @@ def predict_proba(self, X): | |||
| Parameters | |||
| ---------- | |||
| X : array-like of shape (n_samples, n_features) | |||
| Scoring vector, where n_samples is the number of samples and | |||
| n_features is the number of features. | |||
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| n_features is the number of features. | |
| `n_features` is the number of features. |
sklearn/linear_model/_logistic.py
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| @@ -1647,14 +1650,17 @@ def predict_proba(self, X): | |||
| return softmax(decision_2d, copy=False) | |||
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| def predict_log_proba(self, X): | |||
| """Log of probability estimates. | |||
| """ | |||
| Log of probability estimates. | |||
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| Log of probability estimates. | |
| Predict logarithm of probability estimates. |
sklearn/linear_model/_logistic.py
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| The returned estimates for all classes are ordered by the | ||
| label of classes. | ||
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| Parameters | ||
| ---------- | ||
| X : array-like of shape (n_samples, n_features) | ||
| Scoring vector, where n_samples is the number of samples and |
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Suggested change
| Scoring vector, where n_samples is the number of samples and | |
| Vector to be scored, where n_samples is the number of samples and |
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@tolaa001 Thanks!!! |
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Reference Issues/PRs
Contribution to fix part of #15440
What does this implement/fix? Explain your changes.
Ensuring
LogisticRegressionmethods pass NumPy doc validation