How to create and optimize a baseline Decision Tree model for Regression?

How to create and optimize a baseline Decision Tree model for Regression?

How to create and optimize a baseline Decision Tree model for Regression?

This recipe helps you create and optimize a baseline Decision Tree model for Regression

This data science python source code does the following: 1. Imports all the necessary library 2. Creates pipeline for the workflow 3. Applies "Standard Scaler" and "PCA" decomposition 4. Applies decision tree regressor model and optimizes it using GridSearchCV
In [2]:
## How to create and optimize a baseline Decision Tree model for Regression
def Snippet_151():
    print(format('## How to create and optimize a baseline Decision Tree model for Regression','*^82'))

    import warnings

    # load libraries
    from sklearn import decomposition, datasets
    from sklearn import tree
    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.make_regression(n_samples=1000, n_features=20, n_informative=10,
                n_targets=1, bias=0.0, effective_rank=None, tail_strength=0.5, noise=0.2,
                shuffle=True, coef=False, random_state=None)
    X = dataset[0]
    y = dataset[1]

    # Create an scaler object
    sc = StandardScaler()

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

    # Create a logistic regression object with an L2 penalty
    dtreeReg = tree.DecisionTreeRegressor()

    # 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),
                           ('dtreeReg', dtreeReg)])

    # 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 DecisionTreeRegressor
    criterion = ['friedman_mse', 'mse']
    max_depth = [4,6,8,10]

    # 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,

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

    # Fit the grid search, 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()['dtreeReg'])

    # Use Cross Validation To Evaluate Model
    CV_Result = cross_val_score(clf, X, y, cv=3, n_jobs=-1, scoring='r2')
    print(); print(CV_Result)
    print(); print(CV_Result.mean())
    print(); print(CV_Result.std())

***## How to create and optimize a baseline Decision Tree model for Regression****
Best Number Of Components: 13

DecisionTreeRegressor(criterion='friedman_mse', max_depth=4,
           max_features=None, max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, presort=False, random_state=None,

[0.1138055  0.29104455 0.2830292 ]



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