Classification

Classification

1. Decision Tree

   • Components
     • Node
     • Branch
     • Leaves
   • Functions
     • Info Gain
       • ensure how pure is an attribute 
       • Building Decision Tree with Information Gain Choose attribute with the highest information gain​
       • Highly-branching attributes
         • Information gain is biased towards  choosing attributes with a large number of values​ == This may result in over-fitting​ It means the tree is too much -> remove all primary key when analysing ​
     • Info gain ratio
       • reduce bias on highly branching attributes. 
       • makes number and size of branch into account
       • Large: data evenly spread across the branches small: all data belongs to 1 branch
       • Overcompensate
         • Sometiems may not fix info gain problem with higly-branched attributes worse, may overcompenate, choosing attributes juz because their intrisic info is very low.
         • To fix overcompensate: When building a tree: 1. Only consider attributes with greater than average info gain. 2. Remove ID codes like attributes 3. compare on gain ratio
         • Remove ID codes like attributes
         • info gain > average
         • Compare gain ratio
     • Gain Index
   • Industrial-strength algorithms​
     • For a learning algorithm to be useful in a wide-range of real-world applications it must​ Permit numeric attributes​ Allow missing values​ Be robust in the presence of noise​ Be able to approximate arbitrary concept descriptions​
     • Numeric Attributes​
       • Unlike nominal attributes where the value of each branch is clear, numeric attributes have many possible split points​
       • Sort by value​
       • Choose “best” split point
         • Evaluate the goodness measures for every possible split point of attribute​ Choose “best” split point​
         • The goodness measure for that split point then becomes the goodness measure for the attribute​
       • Problems of binary splits
         • not exhausted all info in the attribute
         • may have to be tested several times along each path in the tree
         • Makes tree harder o read
         • Remedy
           • Pre-discretize numeric attributes
             • convert numeric to nominal
           • Use multi-way splits instead of binary ones

2. Association Rules​

   • Sometimes, you do not have a label that you would like to predict​ You would like to know the underlying patterns in your data​ Exploration​
   • You need unsupervised learning​ Clustering​ Association rules​ For example, to know the list of items that most people bought together in the supermarket​
   • Definition
     • Find the most frequent patterns in the dataset

3. Naïve Bayes Classifier

   • Assumption: All attributes are conditionally independent given the class label Y​
     • Cannot handle correlated attributes​
   • Calculate the conditional probability of each possible attribute value given a class label
   • Missing & Numeric Data​
     • With Naïve Bayes​ No need to explicitly model the missing values​ Probabilities are calculated based on observed attributes​
     • With numeric data We need to estimate a mean,  μik , and standard deviation σik based on the values of feature Xi in class yk in the training data​​

4. Laplace Smoothing​

   • avoid overfitting

5. Instance-Based Learning​

   • known as: Case-based, Exemplar-based, Memory-based, Rote learning, Lazy learning, Nearest Neighbour ​
   • Simple idea: label a new point the same as the label of the closest/most similar known point in the training set​
   • Simplest method but testing can be very expensive because we need to do detailed comparisons to all past training instances​
   • Similarity/Distance Metrics​
     • Euclidian distance
       • Normalize value between 0 & 1
       • Norminal attributes
   • Other Distance Metrics
     • Dense data (few attributes with zero values)
       • Euclidean Distance
       • Correlation
         • Used for Bioinformatics Data and Recommendation System     
     • Sparse data
       • Cosinw Similarity
         • Used for Information retrieval, Sentiment analysis     
   • KNN
   • SVM
     • •Given a new point Y, find out its score oScore= w. Y + b If Score > Threshold, class = 1 If Score < (-1 * Threshold), class = -1 Otherwise, we don’t know    
     • Hard vs Soft Margin
       •     •Soft-margin oAlways have a solution oMore robust to outliers oSmoother surface •Hard-margin  Requires guessing what the best cost parameter is    
     • Non-linear SVM

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