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)

我不是女神ヾ 2022-12-23 11:24 126阅读 0赞

**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

--------------------

**相关文章**  
[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_LiR_Lasso_datasets_LiR_Lasso_9_1]  
[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][ML_LiR_Lasso_datasets_LiR_Lasso_9_1 1]

# 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 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70][]

## 输出结果 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 3][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 4][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 5][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 6][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 7][]

## Lasso核心代码 ##

class Lasso Found at: sklearn.linear_model._coordinate_descent
    
    class Lasso(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)
        @_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

[ML_LiR_Lasso_datasets_LiR_Lasso_9_1]: https://yunyaniu.blog.csdn.net/article/details/110100977
[ML_LiR_Lasso_datasets_LiR_Lasso_9_1 1]: https://yunyaniu.blog.csdn.net/article/details/110101377
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