sklearn.dummy.DummyClassifier
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class sklearn.dummy.DummyClassifier(strategy=’stratified’, random_state=None, constant=None)
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DummyClassifier is a classifier that makes predictions using simple rules.
This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
Read more in the User Guide.
Parameters: |
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strategy : str, default=”stratified” -
Strategy to use to generate predictions. -
random_state : int, RandomState instance or None, optional, default=None -
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random . -
constant : int or str or array of shape = [n_outputs] -
The explicit constant as predicted by the “constant” strategy. This parameter is useful only for the “constant” strategy. |
Attributes: |
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classes_ : array or list of array of shape = [n_classes] -
Class labels for each output. -
n_classes_ : array or list of array of shape = [n_classes] -
Number of label for each output. -
class_prior_ : array or list of array of shape = [n_classes] -
Probability of each class for each output. -
n_outputs_ : int, -
Number of outputs. -
outputs_2d_ : bool, -
True if the output at fit is 2d, else false. -
sparse_output_ : bool, -
True if the array returned from predict is to be in sparse CSC format. Is automatically set to True if the input y is passed in sparse format. |
Methods
fit (X, y[, sample_weight]) | Fit the random classifier. |
get_params ([deep]) | Get parameters for this estimator. |
predict (X) | Perform classification on test vectors X. |
predict_log_proba (X) | Return log probability estimates for the test vectors X. |
predict_proba (X) | Return probability estimates for the test vectors X. |
score (X, y[, sample_weight]) | Returns the mean accuracy on the given test data and labels. |
set_params (**params) | Set the parameters of this estimator. |
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__init__(strategy=’stratified’, random_state=None, constant=None)
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fit(X, y, sample_weight=None)
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Fit the random classifier.
Parameters: |
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X : {array-like, object with finite length or shape} -
Training data, requires length = n_samples -
y : array-like, shape = [n_samples] or [n_samples, n_outputs] -
Target values. -
sample_weight : array-like of shape = [n_samples], optional -
Sample weights. |
Returns: |
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self : object |
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
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deep : boolean, optional -
If True, will return the parameters for this estimator and contained subobjects that are estimators. |
Returns: |
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params : mapping of string to any -
Parameter names mapped to their values. |
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predict(X)
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Perform classification on test vectors X.
Parameters: |
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X : {array-like, object with finite length or shape} -
Training data, requires length = n_samples |
Returns: |
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y : array, shape = [n_samples] or [n_samples, n_outputs] -
Predicted target values for X. |
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predict_log_proba(X)
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Return log probability estimates for the test vectors X.
Parameters: |
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X : {array-like, object with finite length or shape} -
Training data, requires length = n_samples |
Returns: |
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P : array-like or list of array-like of shape = [n_samples, n_classes] -
Returns the log probability of the sample for each class in the model, where classes are ordered arithmetically for each output. |
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predict_proba(X)
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Return probability estimates for the test vectors X.
Parameters: |
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X : {array-like, object with finite length or shape} -
Training data, requires length = n_samples |
Returns: |
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P : array-like or list of array-lke of shape = [n_samples, n_classes] -
Returns the probability of the sample for each class in the model, where classes are ordered arithmetically, for each output. |
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score(X, y, sample_weight=None)
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Returns the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters: |
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X : {array-like, None} -
Test samples with shape = (n_samples, n_features) or None. Passing None as test samples gives the same result as passing real test samples, since DummyClassifier operates independently of the sampled observations. -
y : array-like, shape = (n_samples) or (n_samples, n_outputs) -
True labels for X. -
sample_weight : array-like, shape = [n_samples], optional -
Sample weights. |
Returns: |
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score : float -
Mean accuracy of self.predict(X) wrt. y. |
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set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.