ML之LiR&Lasso：基于datasets糖尿病数据集利用LiR和Lasso算法进行(9→1)回归预测(三维图散点图可视化)

目录

基于datasets糖尿病数据集利用LiR和Lasso算法进行(9→1)回归预测(三维图散点图可视化)

设计思路

输出结果

Lasso核心代码

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ML之LiR&Lasso：基于datasets糖尿病数据集利用LiR和Lasso算法进行(9→1)回归预测(三维图散点图可视化)
ML之LiR&Lasso：基于datasets糖尿病数据集利用LiR和Lasso算法进行(9→1)回归预测(三维图散点图可视化)实现

基于datasets糖尿病数据集利用LiR和Lasso算法进行(9→1)回归预测(三维图散点图可视化)

设计思路

输出结果

Lasso核心代码

class Lasso Found at: sklearn.linear_model._coordinate_descent
 
class Lasso(-title class_ inherited__">ElasticNet):
    """Linear Model trained with L1 prior as regularizer (aka the Lasso)
    
    The optimization objective for Lasso is::
    
    (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
    
    Technically the Lasso model is optimizing the same objective function as
    the Elastic Net with ``l1_ratio=1.0`` (no L2 penalty).
    
    Read more in the :ref:`User Guide <lasso>`.
    
    Parameters
    ----------
    alpha : float, default=1.0
    Constant that multiplies the L1 term. Defaults to 1.0.
    ``alpha = 0`` is equivalent to an ordinary least square, solved
    by the :class:`LinearRegression` object. For numerical
    reasons, using ``alpha = 0`` with the ``Lasso`` object is not advised.
    Given this, you should use the :class:`LinearRegression` object.
    
    fit_intercept : bool, default=True
    Whether to calculate the intercept for this model. If set
    to False, no intercept will be used in calculations
    (i.e. data is expected to be centered).
    
    normalize : bool, default=False
    This parameter is ignored when ``fit_intercept`` is set to False.
    If True, the regressors X will be normalized before regression by
    subtracting the mean and dividing by the l2-norm.
    If you wish to standardize, please use
    :class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
    on an estimator with ``normalize=False``.
    
    precompute : 'auto', bool or array-like of shape (n_features, n_features),\
    default=False
    Whether to use a precomputed Gram matrix to speed up
    calculations. If set to ``'auto'`` let us decide. The Gram
    matrix can also be passed as argument. For sparse input
    this option is always ``True`` to preserve sparsity.
    
    copy_X : bool, default=True
    If ``True``, X will be copied; else, it may be overwritten.
    
    max_iter : int, default=1000
    The maximum number of iterations
    
    tol : float, default=1e-4
    The tolerance for the optimization: if the updates are
    smaller than ``tol``, the optimization code checks the
    dual gap for optimality and continues until it is smaller
    than ``tol``.
    
    warm_start : bool, default=False
    When set to True, reuse the solution of the previous call to fit as
    initialization, otherwise, just erase the previous solution.
    See :term:`the Glossary <warm_start>`.
    
    positive : bool, default=False
    When set to ``True``, forces the coefficients to be positive.
    
    random_state : int, RandomState instance, default=None
    The seed of the pseudo random number generator that selects a 
     random
    feature to update. Used when ``selection`` == 'random'.
    Pass an int for reproducible output across multiple function calls.
    See :term:`Glossary <random_state>`.
    
    selection : {'cyclic', 'random'}, default='cyclic'
    If set to 'random', a random coefficient is updated every iteration
    rather than looping over features sequentially by default. This
    (setting to 'random') often leads to significantly faster convergence
    especially when tol is higher than 1e-4.
    
    Attributes
    ----------
    coef_ : ndarray of shape (n_features,) or (n_targets, n_features)
    parameter vector (w in the cost function formula)
    
    sparse_coef_ : sparse matrix of shape (n_features, 1) or \
    (n_targets, n_features)
    ``sparse_coef_`` is a readonly property derived from ``coef_``
    
    intercept_ : float or ndarray of shape (n_targets,)
    independent term in decision function.
    
    n_iter_ : int or list of int
    number of iterations run by the coordinate descent solver to reach
    the specified tolerance.
    
    Examples
    --------
    >>> from sklearn import linear_model
    >>> clf = linear_model.Lasso(alpha=0.1)
    >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
    Lasso(alpha=0.1)
    >>> print(clf.coef_)
    [0.85 0.  ]
    >>> print(clf.intercept_)
    0.15...
    
    See also
    --------
    lars_path
    lasso_path
    LassoLars
    LassoCV
    LassoLarsCV
    sklearn.decomposition.sparse_encode
    
    Notes
    -----
    The algorithm used to fit the model is coordinate descent.
    
    To avoid unnecessary memory duplication the X argument of the fit 
     method
    should be directly passed as a Fortran-contiguous numpy array.
    """
    path = staticmethod(enet_path)
-meta">    @_deprecate_positional_args
    def __init__(self, alpha=1.0, *, fit_intercept=True, normalize=False, 
        precompute=False, copy_X=True, max_iter=1000, 
        tol=1e-4, warm_start=False, positive=False, 
        random_state=None, selection='cyclic'):
        super().__init__(alpha=alpha, l1_ratio=1.0, fit_intercept=fit_intercept, 
         normalize=normalize, precompute=precompute, copy_X=copy_X, 
         max_iter=max_iter, tol=tol, warm_start=warm_start, positive=positive, 
         random_state=random_state, selection=selection)
 
 
 
 Functions for CV with paths functions