Construct a 'DecisionTreeID3 object, passing 'x' and 'y' together in one table.
Construct a 'DecisionTreeID3 object, passing 'x' and 'y' together in one table.
the data vectors along with their classifications stored as rows of a matrix
the names for all features/variables
boolean value to indicate whether according feature is continuous
the number of classes
the names for all classes
the value count array indicating number of distinct values per feature
the data vectors stored as rows of a matrix
the class array, where y_i = class for row i of the matrix x
the names for all features/variables
boolean value to indicate whether according feature is continuous
the number of classes
the names for all classes
the value count array indicating number of distinct values per feature
Class that contains information for a tree node.
Given the next most distinguishing feature/attribute, extend the decision tree.
Given the next most distinguishing feature/attribute, extend the decision tree.
the optimal feature and its gain
Given a continuous feature, adjust its threshold to improve gain.
Given a continuous feature, adjust its threshold to improve gain.
the feature index to consider
Given a data vector z, classify it returning the class number (0, .
Given a data vector z, classify it returning the class number (0, ..., k-1) by following a decision path from the root to a leaf.
the data vector to classify (some continuous features)
Given a data vector z, classify it returning the class number (0, .
Given a data vector z, classify it returning the class number (0, ..., k-1) by following a decision path from the root to a leaf.
the data vector to classify (purely discrete features)
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Given a feature column (e.
Given a feature column (e.g., 2 (Humidity)) and a value (e.g., 1 (High)) use the frequency of ocurrence the value for each classification (e.g., 0 (no), 1 (yes)) to estimate k probabilities. Also, determine the fraction of training cases where the feature has this value (e.g., fraction where Humidity is High = 7/14).
a feature column to consider (e.g., Humidity)
one of the possible values for this feature (e.g., 1 (High))
indicates whether is calculating continuous feature
threshold for continuous feature
Compute the information gain due to using the values of a feature/attribute to distinguish the training cases (e.
Compute the information gain due to using the values of a feature/attribute to distinguish the training cases (e.g., how well does Humidity with its values Normal and High indicate whether one will play tennis).
the feature to consider (e.g., 2 (Humidity))
the number of data vectors in training-set (# rows)
the number of data vectors in training-set (# rows)
the training-set size as a Double
the training-set size as a Double
the number of features/variables (# columns)
the number of features/variables (# columns)
the feature-set size as a Double
the feature-set size as a Double
Return new x matrix and y array for next step of constructing decision tree.
Return new x matrix and y array for next step of constructing decision tree.
the feature index
one of the features values
Print out the decision tree using Breadth First Search (BFS).
Test the quality of the training with a test-set and return the fraction of correct classifications.
Test the quality of the training with a test-set and return the fraction of correct classifications.
the integer-valued test vectors stored as rows of a matrix
the test classification vector, where yy_i = class for row i of xx
Train the classifier, i.
Train the classifier, i.e., determine which feature provides the most information gain and select it as the root of the decision tree.
Return default values for binary input data (value count (vc) set to 2).
Return default values for binary input data (value count (vc) set to 2).
the data vectors stored as rows of a matrix
the class array, where y_i = class for row i of the matrix x
The
DecisionTreeC45class implements a Decision Tree classifier using the C4.5 algorithm. The classifier is trained using a data matrix 'x' and a classification vector 'y'. Each data vector in the matrix is classified into one of 'k' classes numbered '0, ..., k-1'. Each column in the matrix represents a feature (e.g., Humidity). The 'vc' array gives the number of distinct values per feature (e.g., 2 for Humidity).