QuadraticDiscriminantAnalysis
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.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new QuadraticDiscriminantAnalysis(opts?: object): QuadraticDiscriminantAnalysis;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.priors? | ArrayLike | Class priors. By default, the class proportions are inferred from the training data. |
opts.reg_param? | number | Regularizes the per-class covariance estimates by transforming S2 as S2 \= (1 \- reg\_param) \* S2 + reg\_param \* np.eye(n\_features), where S2 corresponds to the scaling\_ attribute of a given class. Default Value 0 |
opts.store_covariance? | boolean | If true, the class covariance matrices are explicitly computed and stored in the self.covariance\_ attribute. Default Value false |
opts.tol? | number | Absolute threshold for a singular value to be considered significant, used to estimate the rank of Xk where Xk is the centered matrix of samples in class k. This parameter does not affect the predictions. It only controls a warning that is raised when features are considered to be colinear. Default Value 0.0001 |
Returns
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:25 (opens in a new tab)
Methods
decision_function()
Apply decision function to an array of samples.
The decision function is equal (up to a constant factor) to the log-posterior of the model, i.e. log p(y \= k | x). In a binary classification setting this instead corresponds to the difference log p(y \= 1 | x) \- log p(y \= 0 | x). See Mathematical formulation of the LDA and QDA classifiers.
Signature
decision_function(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Array of samples (test vectors). |
Returns
Promise<ArrayLike>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:139 (opens in a new tab)
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:120 (opens in a new tab)
fit()
Fit the model according to the given training data and parameters.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training vector, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | ArrayLike | Target values (integers). |
Returns
Promise<any>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:177 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:223 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:71 (opens in a new tab)
predict()
Perform classification on an array of test vectors X.
The predicted class C for each sample in X is returned.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Vector to be scored, where n\_samples is the number of samples and n\_features is the number of features. |
Returns
Promise<ArrayLike>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:263 (opens in a new tab)
predict_log_proba()
Return log of posterior probabilities of classification.
Signature
predict_log_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Array of samples/test vectors. |
Returns
Promise<ArrayLike[]>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:301 (opens in a new tab)
predict_proba()
Return posterior probabilities of classification.
Signature
predict_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Array of samples/test vectors. |
Returns
Promise<ArrayLike[]>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:339 (opens in a new tab)
score()
Return 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.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True labels for X. |
Returns
Promise<number>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:379 (opens in a new tab)
set_score_request()
Request metadata passed to the score method.
Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Signature
set_score_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in score. |
Returns
Promise<any>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:435 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:21 (opens in a new tab)
id
string
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:18 (opens in a new tab)
opts
any
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:19 (opens in a new tab)
Accessors
classes_
Unique class labels.
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:608 (opens in a new tab)
covariance_
For each class, gives the covariance matrix estimated using the samples of that class. The estimations are unbiased. Only present if store\_covariance is true.
Signature
covariance_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:473 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:662 (opens in a new tab)
means_
Class-wise means.
Signature
means_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:500 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:635 (opens in a new tab)
priors_
Class priors (sum to 1).
Signature
priors_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:527 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:58 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:62 (opens in a new tab)
rotations_
For each class k an array of shape (n_features, n_k), where n\_k \= min(n\_features, number of elements in class k) It is the rotation of the Gaussian distribution, i.e. its principal axis. It corresponds to V, the matrix of eigenvectors coming from the SVD of Xk \= U S Vt where Xk is the centered matrix of samples from class k.
Signature
rotations_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:554 (opens in a new tab)
scalings_
For each class, contains the scaling of the Gaussian distributions along its principal axes, i.e. the variance in the rotated coordinate system. It corresponds to S^2 / (n\_samples \- 1), where S is the diagonal matrix of singular values from the SVD of Xk, where Xk is the centered matrix of samples from class k.
Signature
scalings_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/discriminant_analysis/QuadraticDiscriminantAnalysis.ts:581 (opens in a new tab)