Decision Tree Regression:
Decision tree builds regression or classification models in the form of a tree structure.
It breaks down a datasets into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed.
The final result is a tree with decision nodes and leaf nodes. A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast and Rainy), each representing values for the attribute tested.
Leaf node (e.g., Hours Played) represents a decision on the numerical target.
The topmost decision node in a tree which corresponds to the best predictor called root node.
Decision trees can handle both categorical and numerical data.
Project | 05
Project | 05 Decision Tree Regression
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
# Training the Decision Tree Regression model on the whole dataset
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = 0)
regressor.fit(X, y)
# Predicting a new result
regressor.predict([[6.5]])
# Visualising the Decision Tree Regression results (higher resolution)
X_grid = np.arange(min(X), max(X), 0.01)
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.title('Truth or Bluff (Decision Tree Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()