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Learning Decision Tree
Prepared By: Tamboli Tahseen
Roll No. : 2140683
Branch: BEIT-SEM (VII)
M.H.Saboo Siddik College of Engineering
shaikhtahseen6783@gmail.com
Introduction
 Decision tree induction is one of the simplest,
and yet most successful forms of learning
algorithm. It serves as a good introduction to
the area of inductive learning, and is easy to
implement.
 A decision tree takes as input an object or
situation described by a set of attributes and
returns a “decision” the predicted output value
for the input.
 Decision trees represent protocols which can
be easily understood by humans.11/26/2016 Intelligent System 2
Characteristics
 Root node is a starting node.
 Decision tree gives the test to be carried out on
a decision.
 Leaf nodes stand for probable final decision.
 Every node in the tree returns yes/no/probable
decision.
 Branches of the tree are labeled with probable
value.
 Boolean Classification.
11/26/2016 Intelligent System 3
Example
11/26/2016 Intelligent System 4
Attributes
 Alternate
 Bar
 Fri/Sat
 Hungry
 Patrons
 Price
 Raining
 Reservation
 Type
 WaitEstimate
11/26/2016 Intelligent System 5
Training Set
Learning Curve for DT
Algorithm
Function DECISION-TREE-LEARNING(examples, attribs, default)returns a
decision tree
Inputs: examples, set of examples
attribs, set of attribute
default, default value for the goal predicate
If examples is empty then return default
Else if all examples have the same classification then return the classification
Else if attribs is empty then return MAJORITY-VALUE(examples)
Else
best CHOOSE-ATTRIBUTE(attribs, examples)
tree a new decision tree with root test best
m MAJORITY-VALUE(examples)
for each value Vi of best do
examples  {element of examples with best = Vi}
Subtree DECISION-TREE-LEARNING ( examples, attribs --- best,
m)
add a branch to tree with label Vi and subtree subtree
return tree
Applicability of Decision trees
Missing data
Multivalued attributes
Continuous and integer – valued input
attributes
Continuous – valued output attributes
Pros & Cons
 Pros
1. Are simple to understand and interpret. People are able to
understand decision tree models after a brief explanation.
2. Have value even with little hard data. Important insights can be
generated based on experts describing a situation (its alternatives,
probabilities, and costs) and their preferences for outcomes.
3. Help determine worst, best and expected values for different
scenarios
4. Can be combined with other decision techniques.
 Cons
1. For data including categorical variables with different number of
levels, information gain in decision trees are biased in favor of
those attributes with more levels.
2. Calculations can get very complex particularly if many values are
uncertain and/or if many outcomes are linked.
Thank You
11/26/2016 Intelligent System 11

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83 learningdecisiontree

  • 1. Learning Decision Tree Prepared By: Tamboli Tahseen Roll No. : 2140683 Branch: BEIT-SEM (VII) M.H.Saboo Siddik College of Engineering shaikhtahseen6783@gmail.com
  • 2. Introduction  Decision tree induction is one of the simplest, and yet most successful forms of learning algorithm. It serves as a good introduction to the area of inductive learning, and is easy to implement.  A decision tree takes as input an object or situation described by a set of attributes and returns a “decision” the predicted output value for the input.  Decision trees represent protocols which can be easily understood by humans.11/26/2016 Intelligent System 2
  • 3. Characteristics  Root node is a starting node.  Decision tree gives the test to be carried out on a decision.  Leaf nodes stand for probable final decision.  Every node in the tree returns yes/no/probable decision.  Branches of the tree are labeled with probable value.  Boolean Classification. 11/26/2016 Intelligent System 3
  • 5. Attributes  Alternate  Bar  Fri/Sat  Hungry  Patrons  Price  Raining  Reservation  Type  WaitEstimate 11/26/2016 Intelligent System 5
  • 8. Algorithm Function DECISION-TREE-LEARNING(examples, attribs, default)returns a decision tree Inputs: examples, set of examples attribs, set of attribute default, default value for the goal predicate If examples is empty then return default Else if all examples have the same classification then return the classification Else if attribs is empty then return MAJORITY-VALUE(examples) Else best CHOOSE-ATTRIBUTE(attribs, examples) tree a new decision tree with root test best m MAJORITY-VALUE(examples) for each value Vi of best do examples  {element of examples with best = Vi} Subtree DECISION-TREE-LEARNING ( examples, attribs --- best, m) add a branch to tree with label Vi and subtree subtree return tree
  • 9. Applicability of Decision trees Missing data Multivalued attributes Continuous and integer – valued input attributes Continuous – valued output attributes
  • 10. Pros & Cons  Pros 1. Are simple to understand and interpret. People are able to understand decision tree models after a brief explanation. 2. Have value even with little hard data. Important insights can be generated based on experts describing a situation (its alternatives, probabilities, and costs) and their preferences for outcomes. 3. Help determine worst, best and expected values for different scenarios 4. Can be combined with other decision techniques.  Cons 1. For data including categorical variables with different number of levels, information gain in decision trees are biased in favor of those attributes with more levels. 2. Calculations can get very complex particularly if many values are uncertain and/or if many outcomes are linked.