Data Applied: Decision
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Decision trees are models that use divide and conquer techniques to split data into branches based on attribute values at each node. To construct a decision tree, an attribute is selected to label the root node using the maximum information gain approach. Then the process is recursively repeated on the branches. However, highly branching attributes may be favored. To address this, gain ratio is used which considers the split information to compensate for attributes with many splits.
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1) The 1R algorithm generates a one-level decision tree by considering each attribute individually and assigning the majority class to each branch. It chooses the attribute with the minimum classification error.
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3) Decision tree algorithms like ID3 use a divide-and-conquer approach, recursively splitting the data on attributes that maximize information gain or gain ratio at each node.
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Weka is a machine learning software and data mining tool developed at the University of Waikato, New Zealand. It provides algorithms for data preprocessing, classification, clustering, attribute selection, and association rule mining. Weka contains tools for data preprocessing, classification, clustering, attribute selection, association rule mining, and visualization. It has a graphical user interface and can be used for classification, regression, clustering, association rules, and visualization tasks through its Explorer interface.
Weka presentation
Weka is a machine learning software and data mining tool developed at the University of Waikato, New Zealand. It provides algorithms for data preprocessing, classification, clustering, attribute selection, and association rule mining. Weka contains tools for data preprocessing, classification, clustering, visualization, and feature selection. It has a graphical user interface and can be used for classification, regression, clustering, association rules, and visualization.
Decision tree
The document discusses decision tree induction, which is a popular tool for classification and prediction. It describes how decision trees work by having internal decision nodes that split the data into branches, which end at leaf nodes that provide a class label or prediction. It also covers different algorithms for building decision trees like ID3, C4.5, and CART. The key steps in decision tree induction include selecting attributes to split on using metrics like information gain or Gini index, and pruning the fully grown tree to avoid overfitting.
ML .pptx
1. The document describes implementing a decision tree algorithm on a balance scale dataset from UCI to classify data as left, right, or balanced.
2. It discusses decision tree concepts like entropy, information gain, and building and evaluating the model in two phases - building the tree using the training data, and making predictions on test data to calculate accuracy.
3. Code is provided to import the dataset, split into train and test, train decision trees using gini index and entropy, make predictions on test data, and calculate accuracy.
Decision Tree Classification Algorithm.pptx
The document discusses decision tree classification algorithms. It defines key concepts like decision nodes, leaf nodes, splitting, pruning, and describes how a decision tree works. It starts with the root node and uses attribute selection measures like information gain or Gini index to recursively split nodes into subtrees until reaching leaf nodes. Decision trees can model human decision making and have intuitive tree structures, though they may overfit and have complexity issues with many layers.
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Data Applied: Decision
- 2. Introduction Decision trees let you construct decision models They can be used for forecasting, classification or decision At each branch the data is spit based on a particular field of data Decision trees are constructed using Divide and Conquer techniques
- 3. Divide-and-Conquer: Constructing Decision Trees Steps to construct a decision tree recursively: Select an attribute to placed at root node and make one branch for each possible value Repeat the process recursively at each branch, using only those instances that reach the branch If at any time all instances at a node have the classification, stop developing that part of the tree Problem: How to decide which attribute to split on
- 4. Divide-and-Conquer: Constructing Decision Trees Steps to find the attribute to split on: We consider all the possible attributes as option and branch them according to different possible values Now for each possible attribute value we calculate Information and then find the Information gain for each attribute option Select that attribute for division which gives a Maximum Information Gain Do this until each branch terminates at an attribute which gives Information = 0
- 5. Divide-and-Conquer: Constructing Decision Trees Calculation of Information and Gain: For data: (P1, P2, P3……Pn) such that P1 + P2 + P3 +……. +Pn = 1 Information(P1, P2 …..Pn) = -P1logP1 -P2logP2 – P3logP3 ……… -PnlogPn Gain = Information before division – Information after division
- 6. Divide-and-Conquer: Constructing Decision Trees Example: Here we have consider each attribute individually Each is divided into branches according to different possible values Below each branch the number of class is marked
- 7. Divide-and-Conquer: Constructing Decision Trees Calculations: Using the formulae for Information, initially we have Number of instances with class = Yes is 9 Number of instances with class = No is 5 So we have P1 = 9/14 and P2 = 5/14 Info[9/14, 5/14] = -9/14log(9/14) -5/14log(5/14) = 0.940 bits Now for example lets consider Outlook attribute, we observe the following:
- 8. Divide-and-Conquer: Constructing Decision Trees Example Contd. Gain by using Outlook for division = info([9,5]) – info([2,3],[4,0],[3,2]) = 0.940 – 0.693 = 0.247 bits Gain (outlook) = 0.247 bits Gain (temperature) = 0.029 bits Gain (humidity) = 0.152 bits Gain (windy) = 0.048 bits So since Outlook gives maximum gain, we will use it for division And we repeat the steps for Outlook = Sunny and Rainy and stop for Overcast since we have Information = 0 for it
- 9. Divide-and-Conquer: Constructing Decision Trees Highly branching attributes: The problem If we follow the previously subscribed method, it will always favor an attribute with the largest number of branches In extreme cases it will favor an attribute which has different value for each instance: Identification code
- 10. Divide-and-Conquer: Constructing Decision Trees Highly branching attributes: The problem Information for such an attribute is 0 info([0,1]) + info([0,1]) + info([0,1]) + …………. + info([0,1]) = 0 It will hence have the maximum gain and will be chosen for branching But such an attribute is not good for predicting class of an unknown instance nor does it tells anything about the structure of division So we use gain ratio to compensate for this
- 11. Divide-and-Conquer: Constructing Decision Trees Highly branching attributes: Gain ratio Gain ratio = gain/split info To calculate split info, for each instance value we just consider the number of instances covered by each attribute value, irrespective of the class Then we calculate the split info, so for identification code with 14 different values we have: info([1,1,1,…..,1]) = -1/14 x log1/14 x 14 = 3.807 For Outlook we will have the split info: info([5,4,5]) = -1/5 x log 1/5 -1/4 x log1/4 -1/5 x log 1/5 = 1.577
- 12. Decision using Data Applied’s web interface
- 13. Step1: Selection of data
- 15. Step3: Result
- 17. The tutorials section is free, self-guiding and will not involve any additional support.
- 18. Visit us at www.dataminingtools.net