Decision Tree Regression

• CART: Classification and Regression trees. 분류, 회귀 모두에 적용될 수 있는 알고리즘
  • 회귀에서는 종속변수가 연속형 변수

Preprocessing

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings

warnings.filterwarnings('ignore')

dataset = pd.read_csv('_data/04.csv')
x = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values

Modeling

from sklearn.tree import DecisionTreeRegressor

regressor = DecisionTreeRegressor()

# 모델 학습
regressor.fit(x, y)

DecisionTreeRegressor()

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Visualization

x_grid = np.arange(min(x), max(x), 0.1)
x_grid = x_grid.reshape((len(x_grid), 1))
plt.scatter(x, y, color='red')
plt.plot(x_grid, regressor.predict(x_grid), color='blue')
plt.show()