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

0
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()
print(format('## How to create and optimize a baseline Decision Tree model for Regression','*^82'))

import warnings
warnings.filterwarnings("ignore")

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,
dtreeReg__criterion=criterion,
dtreeReg__max_depth=max_depth)

# 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()['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())

Snippet_151()
```
```***## 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,
splitter='best')

[0.1138055  0.29104455 0.2830292 ]

0.22929308183032895

0.08172758772175213
```

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