QuadraticDiscriminantAnalysis
¶
-
class
ibex.sklearn.discriminant_analysis.
QuadraticDiscriminantAnalysis
(priors=None, reg_param=0.0, store_covariance=False, tol=0.0001, store_covariances=None)¶ Bases:
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis
,ibex._base.FrameMixin
Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Quadratic Discriminant Analysis
A classifier with a quadratic decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.
The model fits a Gaussian density to each class.
New in version 0.17: QuadraticDiscriminantAnalysis
Read more in the User Guide.
- priors : array, optional, shape = [n_classes]
- Priors on classes
- reg_param : float, optional
- Regularizes the covariance estimate as
(1-reg_param)*Sigma + reg_param*np.eye(n_features)
- store_covariance : boolean
If True the covariance matrices are computed and stored in the self.covariance_ attribute.
New in version 0.17.
- tol : float, optional, default 1.0e-4
Threshold used for rank estimation.
New in version 0.17.
- covariance_ : list of array-like, shape = [n_features, n_features]
- Covariance matrices of each class.
- means_ : array-like, shape = [n_classes, n_features]
- Class means.
- priors_ : array-like, shape = [n_classes]
- Class priors (sum to 1).
- rotations_ : list of arrays
- For each class k an array of shape [n_features, n_k], with
n_k = min(n_features, number of elements in class k)
It is the rotation of the Gaussian distribution, i.e. its principal axis. - scalings_ : list of arrays
- For each class k an array of shape [n_k]. It contains the scaling of the Gaussian distributions along its principal axes, i.e. the variance in the rotated coordinate system.
>>> from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis >>> import numpy as np >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> clf = QuadraticDiscriminantAnalysis() >>> clf.fit(X, y) ... QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariance=False, store_covariances=None, tol=0.0001) >>> print(clf.predict([[-0.8, -1]])) [1]
- sklearn.discriminant_analysis.LinearDiscriminantAnalysis: Linear
- Discriminant Analysis
-
decision_function
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Apply decision function to an array of samples.
- X : array-like, shape = [n_samples, n_features]
- Array of samples (test vectors).
- C : array, shape = [n_samples, n_classes] or [n_samples,]
- Decision function values related to each class, per sample. In the two-class case, the shape is [n_samples,], giving the log likelihood ratio of the positive class.
- A parameter
-
fit
(X, y)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Fit the model according to the given training data and parameters.
Changed in version 0.19:
store_covariances
has been moved to main constructor asstore_covariance
Changed in version 0.19:
tol
has been moved to main constructor.- X : array-like, shape = [n_samples, n_features]
- Training vector, where n_samples is the number of samples and n_features is the number of features.
- y : array, shape = [n_samples]
- Target values (integers)
- A parameter
-
predict
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Perform classification on an array of test vectors X.
The predicted class C for each sample in X is returned.
X : array-like, shape = [n_samples, n_features]
C : array, shape = [n_samples]
- A parameter
-
predict_log_proba
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Return posterior probabilities of classification.
- X : array-like, shape = [n_samples, n_features]
- Array of samples/test vectors.
- C : array, shape = [n_samples, n_classes]
- Posterior log-probabilities of classification per class.
- A parameter
-
predict_proba
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Return posterior probabilities of classification.
- X : array-like, shape = [n_samples, n_features]
- Array of samples/test vectors.
- C : array, shape = [n_samples, n_classes]
- Posterior probabilities of classification per class.
- A parameter
-
score
(X, y, sample_weight=None)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- X : array-like, shape = (n_samples, n_features)
- Test samples.
- 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.
- score : float
- Mean accuracy of self.predict(X) wrt. y.
- A parameter
- A parameter