decision tree regression
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This document discusses decision tree regression for predicting salary based on position level. It shows how to import data, build a decision tree regression model using scikit-learn in Python and rpart in R, make predictions, and plot the results. It notes that decision trees are discrete models, so the plots need to treat the x-axis as discrete rather than continuous to properly visualize the model's piecewise constant predictions.
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![IMPORTING DATASET
dataset = pd.read_csv(file_path)
X= dataset.iloc[:,1:2].values
y= dataset.iloc[:,2:3].values](https://image.slidesharecdn.com/5-decisiontreeregression-171230051804/85/decision-tree-regression-10-320.jpg)
![READ DATASET
library(readr)
dataset <- read_csv("D:/machine learning AZ/Machine Learning A-Z
Template Folder/Part 2 - Regression/Section 7 - Support Vector
Regression (SVR)/SVR/Position_Salaries.csv")
dataset= dataset[2:3]](https://image.slidesharecdn.com/5-decisiontreeregression-171230051804/85/decision-tree-regression-16-320.jpg)
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If you are hiring Data Scientists, call us at (1)888-752-7585 or reach [email protected] to share your openings and set up interviews with our excellent students.
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Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
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Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
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- 4. The value in green box represents the average of data points in that split
- 7. PYTHON
- 8. READING FILE DYNAMICALLY from tkinter import * from tkinter.filedialog import askopenfilename root = Tk() root.withdraw() root.update() file_path = askopenfilename() root.destroy()
- 9. IMPORTING LIBRARIES import pandas as pd import numpy as np import matplotlib.pyplot as plt
- 10. IMPORTING DATASET dataset = pd.read_csv(file_path) X= dataset.iloc[:,1:2].values y= dataset.iloc[:,2:3].values
- 11. DECISION TREE REGRESSOR from sklearn.tree import DecisionTreeRegressor regressor = DecisionTreeRegressor(random_state=42) model = regressor.fit(X,y)
- 13. SIMPLE PLOT plt.scatter(X,y,color="red") plt.plot(X,model.predict(X),color="blue") plt.title('Truth or Bluff (Decision Tree Regression)') plt.xlabel('Position level') plt.ylabel('Salary') plt.show() NOTE : Whats wrong here ? Well in the simple plot the Decision Tree Regressor model is treated as a c But it is not a continuous model. Decision Tree Regressor is a discrete model hence it should be treate FIX : plotting the same graph with grid with small step size say 0.01 will help us visualize better
- 14. UPDATED PLOT X_grid = np.arange(min(X), max(X), 0.001) 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() Note : Here the graph that is plotted gives us the clear discrete structure
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- 16. READ DATASET library(readr) dataset <- read_csv("D:/machine learning AZ/Machine Learning A-Z Template Folder/Part 2 - Regression/Section 7 - Support Vector Regression (SVR)/SVR/Position_Salaries.csv") dataset= dataset[2:3]
- 17. LIBRARY REQUIRED - RPART library('rpart') regressor = rpart(Salary ~ . , data= dataset , control = rpart.control(minsplit = 1)) NOTE : do not forget to include control parameter as it decides the number of splits in your model.
- 18. PLOT # Visualising the Regression Model results # install.packages('ggplot2') library(ggplot2) ggplot() + geom_point(aes(x = dataset$Level, y = dataset$Salary), colour = 'red') + geom_line(aes(x = dataset$Level, y = predict(regressor, newdata = dataset)), colour = 'blue') + ggtitle('Truth or Bluff (Regression Model)') + xlab('Level') + ylab('Salary') Here model is not treated as discrete hence plot simply joins the prediction points since we don’t have any predictions for this interval. Solution : plot Level as grid with step size as small as 0.01 or whatever you want it to be
- 19. SMOOTHER PLOT # install.packages('ggplot2') library(ggplot2) x_grid = seq(min(dataset$Level), max(dataset$Level), 0.001) ggplot() + geom_point(aes(x = dataset$Level, y = dataset$Salary), colour = 'red') + geom_line(aes(x = x_grid, y = predict(regressor, newdata = data.frame(Level = x_grid))), colour = 'blue') + ggtitle('Truth or Bluff (Regression Model)') + xlab('Level') + ylab('Salary') PERFECT PLOT