**# DECISION TREE CLASSIFICATION EXAMPLE:**

import pandas as pd
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y = True)

**# Decision Trees for Classification**
# Decision Tree Classifier is used with categorical data

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33)

model = DecisionTreeClassifier()
model.fit(X_train, y_train)
model.score(X_test, y_test)

r2 score = 0.96

DecisionTreeClassifier()
# min_samples_split
# min_samples_leaf
# max_depth
# max_features

**# Decision Trees for Regression**
# Decision Tree Classifier is used with numerical data

from sklearn.tree import DecisionTreeRegressor

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33)

model = DecisionTreeRegressor()
model.fit(X_train, y_train)
model.score(X_test, y_test)

r2 score = 0.9410377358490566

DecisionTreeRegressor()
# min_samples_split
# min_samples_leaf
# max_depth
# max_features