How to create and optimize a baseline Lasso Regression model?
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How to create and optimize a baseline Lasso Regression model?

How to create and optimize a baseline Lasso Regression model?

This recipe helps you create and optimize a baseline Lasso Regression model

0
This data science python source code does the following: 1. Imports datasets from sklearn library 2. Creates pipeline for visualizing roadmap of the code 3. Performs Standard scaling and decomposition for dimensionality reduction 4. Applies lasso Regression model and performs GridSearchCV for optimization of parameters
In [2]:
## How to create and optimize a baseline Lasso Regression model
def Snippet_149():
    print()
    print(format('How to create and optimize a baseline Lasso regression model','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import decomposition, datasets
    from sklearn import linear_model
    from sklearn.pipeline import Pipeline
    from sklearn.model_selection import GridSearchCV, cross_val_score
    from sklearn.preprocessing import StandardScaler

    # Load the iris flower data
    dataset = datasets.load_boston()
    X = dataset.data
    y = dataset.target

    # Create an scaler object
    sc = StandardScaler()

    # Create a pca object
    pca = decomposition.PCA()

    # Create a logistic regression object with an L2 penalty
    lasso = linear_model.Lasso()

    # Create a pipeline of three steps. First, standardize the data.
    # Second, tranform the data with PCA.
    # Third, train a Decision Tree Classifier on the data.
    pipe = Pipeline(steps=[('sc', sc),
                           ('pca', pca),
                           ('lasso', lasso)])

    # Create Parameter Space
    # Create a list of a sequence of integers from 1 to 30 (the number of features in X + 1)
    n_components = list(range(1,X.shape[1]+1,1))

    # Create lists of parameter for Lasso Regression
    normalize = [True, False]
    selection = ['cyclic', 'random']

    # Create a dictionary of all the parameter options 
    # Note has you can access the parameters of steps of a pipeline by using '__’
    parameters = dict(pca__n_components=n_components,
                      lasso__normalize=normalize,
                      lasso__selection=selection)

    # Conduct Parameter Optmization With Pipeline
    # Create a grid search object
    clf = GridSearchCV(pipe, parameters)

    # Fit the grid search
    clf.fit(X, y)

    # View The Best Parameters
    print('Best Number Of Components:', clf.best_estimator_.get_params()['pca__n_components'])
    print(); print(clf.best_estimator_.get_params()['lasso'])

    # Use Cross Validation To Evaluate Model
    CV_Result = cross_val_score(clf, X, y, cv=10, n_jobs=-1, scoring='r2')
    print(); print(CV_Result)
    print(); print(CV_Result.mean())
    print(); print(CV_Result.std())

Snippet_149()
***********How to create and optimize a baseline Lasso regression model***********
Best Number Of Components: 5

Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,
   normalize=False, positive=False, precompute=False, random_state=None,
   selection='random', tol=0.0001, warm_start=False)

[ 0.62165111  0.63662955 -0.5470779   0.38231819  0.47040916  0.479514
  0.0981076   0.1841878  -0.73314519  0.50955313]

0.21021474338674134

0.4568611690239404

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