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class sklearn.linear_model.LarsCV(*, fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000, n_jobs=None, eps=2.220446049250313e-16, copy_X=True) [source]
Cross-validated Least Angle Regression model. See glossary entry for cross-validation estimator. Read more ... | sklearn.modules.generated.sklearn.linear_model.larscv#sklearn.linear_model.LarsCV |
sklearn.linear_model.LarsCV
class sklearn.linear_model.LarsCV(*, fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000, n_jobs=None, eps=2.220446049250313e-16, copy_X=True) [source]
Cross-validated Least Angle Regression model. See glossary entry for cross-va... | sklearn.modules.generated.sklearn.linear_model.larscv |
fit(X, y) [source]
Fit the model using X, y as training data. Parameters
Xarray-like of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,)
Target values. Returns
selfobject
returns an instance of self. | sklearn.modules.generated.sklearn.linear_model.larscv#sklearn.linear_model.LarsCV.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.larscv#sklearn.linear_model.LarsCV.get_params |
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | sklearn.modules.generated.sklearn.linear_model.larscv#sklearn.linear_model.LarsCV.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.linear_model.larscv#sklearn.linear_model.LarsCV.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.larscv#sklearn.linear_model.LarsCV.set_params |
sklearn.linear_model.lars_path(X, y, Xy=None, *, Gram=None, max_iter=500, alpha_min=0, method='lar', copy_X=True, eps=2.220446049250313e-16, copy_Gram=True, verbose=0, return_path=True, return_n_iter=False, positive=False) [source]
Compute Least Angle Regression or Lasso path using LARS algorithm [1] The optimization... | sklearn.modules.generated.sklearn.linear_model.lars_path#sklearn.linear_model.lars_path |
sklearn.linear_model.lars_path_gram(Xy, Gram, *, n_samples, max_iter=500, alpha_min=0, method='lar', copy_X=True, eps=2.220446049250313e-16, copy_Gram=True, verbose=0, return_path=True, return_n_iter=False, positive=False) [source]
lars_path in the sufficient stats mode [1] The optimization objective for the case met... | sklearn.modules.generated.sklearn.linear_model.lars_path_gram#sklearn.linear_model.lars_path_gram |
class sklearn.linear_model.Lasso(alpha=1.0, *, fit_intercept=True, normalize=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic') [source]
Linear Model trained with L1 prior as regularizer (aka the Lasso) The optimization objective f... | sklearn.modules.generated.sklearn.linear_model.lasso#sklearn.linear_model.Lasso |
sklearn.linear_model.Lasso
class sklearn.linear_model.Lasso(alpha=1.0, *, fit_intercept=True, normalize=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic') [source]
Linear Model trained with L1 prior as regularizer (aka the Lasso)... | sklearn.modules.generated.sklearn.linear_model.lasso |
fit(X, y, sample_weight=None, check_input=True) [source]
Fit model with coordinate descent. Parameters
X{ndarray, sparse matrix} of (n_samples, n_features)
Data.
y{ndarray, sparse matrix} of shape (n_samples,) or (n_samples, n_targets)
Target. Will be cast to X’s dtype if necessary.
sample_weightfloat or ... | sklearn.modules.generated.sklearn.linear_model.lasso#sklearn.linear_model.Lasso.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.lasso#sklearn.linear_model.Lasso.get_params |
static path(*args, **kwargs) [source]
Compute elastic net path with coordinate descent. The elastic net optimization function varies for mono and multi-outputs. For mono-output tasks it is: 1 / (2 * n_samples) * ||y - Xw||^2_2
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
For multi-output t... | sklearn.modules.generated.sklearn.linear_model.lasso#sklearn.linear_model.Lasso.path |
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | sklearn.modules.generated.sklearn.linear_model.lasso#sklearn.linear_model.Lasso.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.linear_model.lasso#sklearn.linear_model.Lasso.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.lasso#sklearn.linear_model.Lasso.set_params |
property sparse_coef_
Sparse representation of the fitted coef_. | sklearn.modules.generated.sklearn.linear_model.lasso#sklearn.linear_model.Lasso.sparse_coef_ |
class sklearn.linear_model.LassoCV(*, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, precompute='auto', max_iter=1000, tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=None, positive=False, random_state=None, selection='cyclic') [source]
Lasso linear model with iterative fitting alo... | sklearn.modules.generated.sklearn.linear_model.lassocv#sklearn.linear_model.LassoCV |
sklearn.linear_model.LassoCV
class sklearn.linear_model.LassoCV(*, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, precompute='auto', max_iter=1000, tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=None, positive=False, random_state=None, selection='cyclic') [source]
Lasso linear m... | sklearn.modules.generated.sklearn.linear_model.lassocv |
fit(X, y) [source]
Fit linear model with coordinate descent. Fit is on grid of alphas and best alpha estimated by cross-validation. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If y is mo... | sklearn.modules.generated.sklearn.linear_model.lassocv#sklearn.linear_model.LassoCV.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.lassocv#sklearn.linear_model.LassoCV.get_params |
static path(*args, **kwargs) [source]
Compute Lasso path with coordinate descent The Lasso optimization function varies for mono and multi-outputs. For mono-output tasks it is: (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
For multi-output tasks it is: (1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||... | sklearn.modules.generated.sklearn.linear_model.lassocv#sklearn.linear_model.LassoCV.path |
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | sklearn.modules.generated.sklearn.linear_model.lassocv#sklearn.linear_model.LassoCV.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.linear_model.lassocv#sklearn.linear_model.LassoCV.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.lassocv#sklearn.linear_model.LassoCV.set_params |
class sklearn.linear_model.LassoLars(alpha=1.0, *, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False, jitter=None, random_state=None) [source]
Lasso model fit with Least Angle Regression a.k.a. Lars It is a Linear ... | sklearn.modules.generated.sklearn.linear_model.lassolars#sklearn.linear_model.LassoLars |
sklearn.linear_model.LassoLars
class sklearn.linear_model.LassoLars(alpha=1.0, *, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False, jitter=None, random_state=None) [source]
Lasso model fit with Least Angle Regre... | sklearn.modules.generated.sklearn.linear_model.lassolars |
fit(X, y, Xy=None) [source]
Fit the model using X, y as training data. Parameters
Xarray-like of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Xyarray-like of shape (n_samples,) or (n_samples, n_targets), default=None
Xy = np.dot(... | sklearn.modules.generated.sklearn.linear_model.lassolars#sklearn.linear_model.LassoLars.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.lassolars#sklearn.linear_model.LassoLars.get_params |
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | sklearn.modules.generated.sklearn.linear_model.lassolars#sklearn.linear_model.LassoLars.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.linear_model.lassolars#sklearn.linear_model.LassoLars.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.lassolars#sklearn.linear_model.LassoLars.set_params |
class sklearn.linear_model.LassoLarsCV(*, fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000, n_jobs=None, eps=2.220446049250313e-16, copy_X=True, positive=False) [source]
Cross-validated Lasso, using the LARS algorithm. See glossary entry for cross-validati... | sklearn.modules.generated.sklearn.linear_model.lassolarscv#sklearn.linear_model.LassoLarsCV |
sklearn.linear_model.LassoLarsCV
class sklearn.linear_model.LassoLarsCV(*, fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000, n_jobs=None, eps=2.220446049250313e-16, copy_X=True, positive=False) [source]
Cross-validated Lasso, using the LARS algorithm. Se... | sklearn.modules.generated.sklearn.linear_model.lassolarscv |
fit(X, y) [source]
Fit the model using X, y as training data. Parameters
Xarray-like of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,)
Target values. Returns
selfobject
returns an instance of self. | sklearn.modules.generated.sklearn.linear_model.lassolarscv#sklearn.linear_model.LassoLarsCV.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.lassolarscv#sklearn.linear_model.LassoLarsCV.get_params |
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | sklearn.modules.generated.sklearn.linear_model.lassolarscv#sklearn.linear_model.LassoLarsCV.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.linear_model.lassolarscv#sklearn.linear_model.LassoLarsCV.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.lassolarscv#sklearn.linear_model.LassoLarsCV.set_params |
class sklearn.linear_model.LassoLarsIC(criterion='aic', *, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, positive=False) [source]
Lasso model fit with Lars using BIC or AIC for model selection The optimization objective for Lasso is: (1 / (... | sklearn.modules.generated.sklearn.linear_model.lassolarsic#sklearn.linear_model.LassoLarsIC |
sklearn.linear_model.LassoLarsIC
class sklearn.linear_model.LassoLarsIC(criterion='aic', *, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, positive=False) [source]
Lasso model fit with Lars using BIC or AIC for model selection The optimiza... | sklearn.modules.generated.sklearn.linear_model.lassolarsic |
fit(X, y, copy_X=None) [source]
Fit the model using X, y as training data. Parameters
Xarray-like of shape (n_samples, n_features)
training data.
yarray-like of shape (n_samples,)
target values. Will be cast to X’s dtype if necessary
copy_Xbool, default=None
If provided, this parameter will override the... | sklearn.modules.generated.sklearn.linear_model.lassolarsic#sklearn.linear_model.LassoLarsIC.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.lassolarsic#sklearn.linear_model.LassoLarsIC.get_params |
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | sklearn.modules.generated.sklearn.linear_model.lassolarsic#sklearn.linear_model.LassoLarsIC.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.linear_model.lassolarsic#sklearn.linear_model.LassoLarsIC.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.lassolarsic#sklearn.linear_model.LassoLarsIC.set_params |
sklearn.linear_model.lasso_path(X, y, *, eps=0.001, n_alphas=100, alphas=None, precompute='auto', Xy=None, copy_X=True, coef_init=None, verbose=False, return_n_iter=False, positive=False, **params) [source]
Compute Lasso path with coordinate descent The Lasso optimization function varies for mono and multi-outputs. F... | sklearn.modules.generated.sklearn.linear_model.lasso_path#sklearn.linear_model.lasso_path |
class sklearn.linear_model.LinearRegression(*, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None, positive=False) [source]
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets i... | sklearn.modules.generated.sklearn.linear_model.linearregression#sklearn.linear_model.LinearRegression |
sklearn.linear_model.LinearRegression
class sklearn.linear_model.LinearRegression(*, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None, positive=False) [source]
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum o... | sklearn.modules.generated.sklearn.linear_model.linearregression |
fit(X, y, sample_weight=None) [source]
Fit linear model. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values. Will be cast to X’s dtype if necessary
sample_weightarray-like of shape (n_samples,), d... | sklearn.modules.generated.sklearn.linear_model.linearregression#sklearn.linear_model.LinearRegression.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.linearregression#sklearn.linear_model.LinearRegression.get_params |
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | sklearn.modules.generated.sklearn.linear_model.linearregression#sklearn.linear_model.LinearRegression.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.linear_model.linearregression#sklearn.linear_model.LinearRegression.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.linearregression#sklearn.linear_model.LinearRegression.set_params |
class sklearn.linear_model.LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None) [source]
Logistic Regression (aka logi... | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression |
sklearn.linear_model.LogisticRegression
class sklearn.linear_model.LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None... | sklearn.modules.generated.sklearn.linear_model.logisticregression |
decision_function(X) [source]
Predict confidence scores for samples. The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
array, shape=(n_samples,) if n_classes == 2... | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.decision_function |
densify() [source]
Convert coefficient matrix to dense array format. Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op. Returns
self
... | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.densify |
fit(X, y, sample_weight=None) [source]
Fit the model according to the given training data. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target vec... | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.get_params |
predict(X) [source]
Predict class labels for samples in X. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape [n_samples]
Predicted class label per sample. | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.predict |
predict_log_proba(X) [source]
Predict logarithm of probability estimates. The returned estimates for all classes are ordered by the label of classes. Parameters
Xarray-like of shape (n_samples, n_features)
Vector to be scored, where n_samples is the number of samples and n_features is the number of features. ... | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.predict_log_proba |
predict_proba(X) [source]
Probability estimates. The returned estimates for all classes are ordered by the label of classes. For a multi_class problem, if multi_class is set to be “multinomial” the softmax function is used to find the predicted probability of each class. Else use a one-vs-rest approach, i.e calculate... | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.predict_proba |
score(X, y, sample_weight=None) [source]
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. Parameters
Xarray-like of shape (n_samples, n_featur... | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.set_params |
sparsify() [source]
Convert coefficient matrix to sparse format. Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation. The intercept_ member is not converted. Returns
self
Fitted estimator. ... | sklearn.modules.generated.sklearn.linear_model.logisticregression#sklearn.linear_model.LogisticRegression.sparsify |
class sklearn.linear_model.LogisticRegressionCV(*, Cs=10, fit_intercept=True, cv=None, dual=False, penalty='l2', scoring=None, solver='lbfgs', tol=0.0001, max_iter=100, class_weight=None, n_jobs=None, verbose=0, refit=True, intercept_scaling=1.0, multi_class='auto', random_state=None, l1_ratios=None) [source]
Logisti... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV |
sklearn.linear_model.LogisticRegressionCV
class sklearn.linear_model.LogisticRegressionCV(*, Cs=10, fit_intercept=True, cv=None, dual=False, penalty='l2', scoring=None, solver='lbfgs', tol=0.0001, max_iter=100, class_weight=None, n_jobs=None, verbose=0, refit=True, intercept_scaling=1.0, multi_class='auto', random_st... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv |
decision_function(X) [source]
Predict confidence scores for samples. The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
array, shape=(n_samples,) if n_classes == 2... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.decision_function |
densify() [source]
Convert coefficient matrix to dense array format. Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op. Returns
self
... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.densify |
fit(X, y, sample_weight=None) [source]
Fit the model according to the given training data. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target vec... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.get_params |
predict(X) [source]
Predict class labels for samples in X. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape [n_samples]
Predicted class label per sample. | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.predict |
predict_log_proba(X) [source]
Predict logarithm of probability estimates. The returned estimates for all classes are ordered by the label of classes. Parameters
Xarray-like of shape (n_samples, n_features)
Vector to be scored, where n_samples is the number of samples and n_features is the number of features. ... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.predict_log_proba |
predict_proba(X) [source]
Probability estimates. The returned estimates for all classes are ordered by the label of classes. For a multi_class problem, if multi_class is set to be “multinomial” the softmax function is used to find the predicted probability of each class. Else use a one-vs-rest approach, i.e calculate... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.predict_proba |
score(X, y, sample_weight=None) [source]
Returns the score using the scoring option on the given test data and labels. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples.
yarray-like of shape (n_samples,)
True labels for X.
sample_weightarray-like of shape (n_samples,), default=None
Sa... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.set_params |
sparsify() [source]
Convert coefficient matrix to sparse format. Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation. The intercept_ member is not converted. Returns
self
Fitted estimator. ... | sklearn.modules.generated.sklearn.linear_model.logisticregressioncv#sklearn.linear_model.LogisticRegressionCV.sparsify |
class sklearn.linear_model.MultiTaskElasticNet(alpha=1.0, *, l1_ratio=0.5, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, random_state=None, selection='cyclic') [source]
Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer. The optimization objective... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnet#sklearn.linear_model.MultiTaskElasticNet |
sklearn.linear_model.MultiTaskElasticNet
class sklearn.linear_model.MultiTaskElasticNet(alpha=1.0, *, l1_ratio=0.5, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, random_state=None, selection='cyclic') [source]
Multi-task ElasticNet model trained with L1/L2 mixed-norm... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnet |
fit(X, y) [source]
Fit MultiTaskElasticNet model with coordinate descent Parameters
Xndarray of shape (n_samples, n_features)
Data.
yndarray of shape (n_samples, n_tasks)
Target. Will be cast to X’s dtype if necessary. Notes Coordinate descent is an algorithm that considers each column of data at a time... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnet#sklearn.linear_model.MultiTaskElasticNet.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnet#sklearn.linear_model.MultiTaskElasticNet.get_params |
static path(*args, **kwargs) [source]
Compute elastic net path with coordinate descent. The elastic net optimization function varies for mono and multi-outputs. For mono-output tasks it is: 1 / (2 * n_samples) * ||y - Xw||^2_2
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
For multi-output t... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnet#sklearn.linear_model.MultiTaskElasticNet.path |
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnet#sklearn.linear_model.MultiTaskElasticNet.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnet#sklearn.linear_model.MultiTaskElasticNet.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnet#sklearn.linear_model.MultiTaskElasticNet.set_params |
property sparse_coef_
Sparse representation of the fitted coef_. | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnet#sklearn.linear_model.MultiTaskElasticNet.sparse_coef_ |
class sklearn.linear_model.MultiTaskElasticNetCV(*, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, max_iter=1000, tol=0.0001, cv=None, copy_X=True, verbose=0, n_jobs=None, random_state=None, selection='cyclic') [source]
Multi-task L1/L2 ElasticNet with built-in cross-validati... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnetcv#sklearn.linear_model.MultiTaskElasticNetCV |
sklearn.linear_model.MultiTaskElasticNetCV
class sklearn.linear_model.MultiTaskElasticNetCV(*, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, max_iter=1000, tol=0.0001, cv=None, copy_X=True, verbose=0, n_jobs=None, random_state=None, selection='cyclic') [source]
Multi-task ... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnetcv |
fit(X, y) [source]
Fit linear model with coordinate descent. Fit is on grid of alphas and best alpha estimated by cross-validation. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If y is mo... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnetcv#sklearn.linear_model.MultiTaskElasticNetCV.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnetcv#sklearn.linear_model.MultiTaskElasticNetCV.get_params |
static path(*args, **kwargs) [source]
Compute elastic net path with coordinate descent. The elastic net optimization function varies for mono and multi-outputs. For mono-output tasks it is: 1 / (2 * n_samples) * ||y - Xw||^2_2
+ alpha * l1_ratio * ||w||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||w||^2_2
For multi-output t... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnetcv#sklearn.linear_model.MultiTaskElasticNetCV.path |
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnetcv#sklearn.linear_model.MultiTaskElasticNetCV.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnetcv#sklearn.linear_model.MultiTaskElasticNetCV.score |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.linear_model.multitaskelasticnetcv#sklearn.linear_model.MultiTaskElasticNetCV.set_params |
class sklearn.linear_model.MultiTaskLasso(alpha=1.0, *, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, random_state=None, selection='cyclic') [source]
Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer. The optimization objective for Lasso is: (1 / (2 *... | sklearn.modules.generated.sklearn.linear_model.multitasklasso#sklearn.linear_model.MultiTaskLasso |
sklearn.linear_model.MultiTaskLasso
class sklearn.linear_model.MultiTaskLasso(alpha=1.0, *, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, random_state=None, selection='cyclic') [source]
Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer. The optimiza... | sklearn.modules.generated.sklearn.linear_model.multitasklasso |
fit(X, y) [source]
Fit MultiTaskElasticNet model with coordinate descent Parameters
Xndarray of shape (n_samples, n_features)
Data.
yndarray of shape (n_samples, n_tasks)
Target. Will be cast to X’s dtype if necessary. Notes Coordinate descent is an algorithm that considers each column of data at a time... | sklearn.modules.generated.sklearn.linear_model.multitasklasso#sklearn.linear_model.MultiTaskLasso.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.linear_model.multitasklasso#sklearn.linear_model.MultiTaskLasso.get_params |
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