Rain Cool Normal Strong No 7 Overcast Cool Normal Strong Yes 8 Sunny Mild High

Rain cool normal strong no 7 overcast cool normal

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Rain Cool Normal Strong No 7 Overcast Cool Normal Strong Yes 8 Sunny Mild High Weak No 9 Sunny Cool Normal Weak Yes 10 Rain Mild Normal Weak Yes 11 Sunny Mild Normal Strong Yes 12 Overcast Mild High Strong Yes 13 Overcast Hot Normal Weak Yes 14 Rain Mild High Strong No
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Gini Index If a data set T contains examples from n classes, gini index, gini(T) is defined as Gini(T) = 1 - where is the relative frequency of class j in T. OR:- Gini = 1 – for i=1 to number of classes
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Final Tree
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What happens in case of regression?
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Solution The target variable is numerical instead of discrete Two approaches:- - Either discretize the numerical target variable e.g., equal-width intervals, or equal-frequency - Or use a classification learning algorithm Adapt the classification algorithm to regression data
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Example Data Day Outlook Temp. Humidity Wind Golf Players 1 Sunny Hot High Weak 25 2 Sunny Hot High Strong 30 3 Overcast Hot High Weak 46 4 Rain Mild High Weak 45 5 Rain Cool Normal Weak 52 6 Rain Cool Normal Strong 23 7 Overcast Cool Normal Strong 43 8 Sunny Mild High Weak 35 9 Sunny Cool Normal Weak 38 10 Rain Mild Normal Weak 46 11 Sunny Mild Normal Strong 48 12 Overcast Mild High Strong 52 13 Overcast Hot Normal Weak 44 14 Rain Mild High Strong 30
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Steps 1. Find Standard Deviation for whole dataset. 2. Repeat it for each feature of an attribute.
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  • Fall '17
  • Mark Isbel

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