View source for sklearn.linear model.MultiTaskLasso

A [[sklearn.linear model.MultiTaskLasso]] is an [[Multi-Task Lasso System]] within <code>[[sklearn.linear_model]]</code> [[Method Class|class]].
* <B>Context:</B>
** Usage:
::: 1) Import [[MultiTaskLasso]] model from [[scikit-learn]] : <code>from [[sklearn.linear_model]] import [[MultiTaskLasso]]</code>
::: 2) Create [[design matrix]] <code>X</code> and [[response vector]] <code>Y</code>
::: 3) Create [[MultiTaskLasso]] object: <code>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])</code>
::: 4)  Choose method(s):
::::* <code>fit(X, y)</code>, fits [[MultiTaskElasticNet]] model with [[coordinate descent]].
::::* <code>get_params([deep])</code>, gets [[parameter]]s for this [[estimator]].
::::* <code>path(X, y[, l1_ratio, eps, n_alphas, ...])</code>, computes [[elastic net]] path with [[coordinate descent]].
::::* <code>predict(X)</code>, [[predict]]s using the [[linear model]].
::::* <code>score(X, y[, sample_weight])</code>, returns the [[coefficient of determination]] R^2 of the prediction.
::::* <code>set_params(**params),</code>, sets the parameters of this estimator.
* <B>Example(s):</B>
** [http://scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html Joint feature selection with multi-task Lasso]
* <B>Counter-Example(s):</B>
** [[sklearn.linear model.LinearRegression]]
** [[sklearn.linear_model.Lasso]] 
** [[sklearn.linear_model.Ridge]].
** [[sklearn.linear_model.ARDRegression]].
** [[sklearn.linear_model.ElasticNet]].
** [[sklearn.linear_model.HuberRegressor]].
** [[sklearn.linear_model.Lars]].
* <B>See:</B> [[Regression System]], [[L1 Norm]], [[L2 Norm]] [[Cross-Validation Task]], [[Ridge Regression Task]], [[Bayesian Analysis]].

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