python-scikit-learnGet Lasso model coefficients

from sklearn import datasets, linear_model, model_selection

X, y = datasets.load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = linear_model.Lasso()
model.fit(X_train, y_train)

coeffs = model.coef_
sparse = model.sparse_coef_
intercept = model.intercept_ctrl + cgithub

`from sklearn import` import module from scikit-learn	`datasets.load_diabetes` loads sample diabetes database
`model_selection.train_test_split` splits given `X` and `y` datasets to test (25% of values by default) and train (75% of values by default) subsets	`.Lasso(` create Lasso model object
`.fit(` train model with a given features and target variable dataset	`.coef_` returns list of coefficients of a trained model
`.intercept_` value of linear model intercept after training	`sparse_coef_` sparse representation of the fitted coefficients

Usage example

from sklearn import datasets, linear_model, model_selection

X, y = datasets.load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = linear_model.Lasso()
model.fit(X_train, y_train)

coeffs = model.coef_
sparse = model.sparse_coef_
intercept = model.intercept_  

print(coeffs, sparse, intercept)

output

[  0.          -0.         378.42448976  22.84904289   0.
   0.          -0.           0.         302.34461535   0.        ]   (0, 2) 378.4244897581937
  (0, 3)    22.8490428882718
  (0, 8)    302.3446153480934 151.67182691818425

Using Lasso model example

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python-scikit-learnGet Lasso model coefficients

Usage example

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