- Big Data Analytics Tutorial
- Big Data Analytics - Home
- Big Data Analytics - Overview
- Big Data Analytics - Data Life Cycle
- Big Data Analytics - Methodology
- Core Deliverables
- Key Stakeholders
- Big Data Analytics - Data Analyst
- Big Data Analytics - Data Scientist

- Big Data Analytics Project
- Data Analytics - Problem Definition
- Big Data Analytics - Data Collection
- Big Data Analytics - Cleansing data
- Big Data Analytics - Summarizing
- Big Data Analytics - Data Exploration
- Data Visualization

- Big Data Analytics Methods
- Big Data Analytics - Introduction to R
- Data Analytics - Introduction to SQL
- Big Data Analytics - Charts & Graphs
- Big Data Analytics - Data Tools
- Data Analytics - Statistical Methods

- Advanced Methods
- Machine Learning for Data Analysis
- Naive Bayes Classifier
- K-Means Clustering
- Association Rules
- Big Data Analytics - Decision Trees
- Logistic Regression
- Big Data Analytics - Time Series
- Big Data Analytics - Text Analytics
- Big Data Analytics - Online Learning

- Big Data Analytics Useful Resources
- Big Data Analytics - Quick Guide
- Big Data Analytics - Resources
- Big Data Analytics - Discussion

# Big Data Analytics - Decision Trees

A Decision Tree is an algorithm used for supervised learning problems such as classification or regression. A decision tree or a classification tree is a tree in which each internal (nonleaf) node is labeled with an input feature. The arcs coming from a node labeled with a feature are labeled with each of the possible values of the feature. Each leaf of the tree is labeled with a class or a probability distribution over the classes.

A tree can be "learned" by splitting the source set into subsets based on an attribute value test. This process is repeated on each derived subset in a recursive manner called **recursive partitioning**. The recursion is completed when the subset at a node has all the same value of the target variable, or when splitting no longer adds value to the predictions. This process of top-down induction of decision trees is an example of a greedy algorithm, and it is the most common strategy for learning decision trees.

Decision trees used in data mining are of two main types −

**Classification tree**− when the response is a nominal variable, for example if an email is spam or not.**Regression tree**− when the predicted outcome can be considered a real number (e.g. the salary of a worker).

Decision trees are a simple method, and as such has some problems. One of this issues is the high variance in the resulting models that decision trees produce. In order to alleviate this problem, ensemble methods of decision trees were developed. There are two groups of ensemble methods currently used extensively −

**Bagging decision trees**− These trees are used to build multiple decision trees by repeatedly resampling training data with replacement, and voting the trees for a consensus prediction. This algorithm has been called random forest.**Boosting decision trees**− Gradient boosting combines weak learners; in this case, decision trees into a single strong learner, in an iterative fashion. It fits a weak tree to the data and iteratively keeps fitting weak learners in order to correct the error of the previous model.

# Install the party package # install.packages('party') library(party) library(ggplot2) head(diamonds) # We will predict the cut of diamonds using the features available in the diamonds dataset. ct = ctree(cut ~ ., data = diamonds) # plot(ct, main="Conditional Inference Tree") # Example output # Response: cut # Inputs: carat, color, clarity, depth, table, price, x, y, z # Number of observations: 53940 # # 1) table <= 57; criterion = 1, statistic = 10131.878 # 2) depth <= 63; criterion = 1, statistic = 8377.279 # 3) table <= 56.4; criterion = 1, statistic = 226.423 # 4) z <= 2.64; criterion = 1, statistic = 70.393 # 5) clarity <= VS1; criterion = 0.989, statistic = 10.48 # 6) color <= E; criterion = 0.997, statistic = 12.829 # 7)* weights = 82 # 6) color > E #Table of prediction errors table(predict(ct), diamonds$cut) # Fair Good Very Good Premium Ideal # Fair 1388 171 17 0 14 # Good 102 2912 499 26 27 # Very Good 54 998 3334 249 355 # Premium 44 711 5054 11915 1167 # Ideal 22 114 3178 1601 19988 # Estimated class probabilities probs = predict(ct, newdata = diamonds, type = "prob") probs = do.call(rbind, probs) head(probs)