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StatsToDo : Classification by Naive Bayes Probability Program

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Related link :
Classification by Bayes Probability Explained Page
Classification by Basic Bayes Probability Program Page

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Data: Attributes ± Outcome Designation

Array of of Apriori Probabilities

Array of Costs Coefficients

Data Input for Analysis of reference Data
    The data is for a table with 2 columns
    Each row contains data from a case from the reference data
    Col 1 = Text string representing attributes of predictor
    Col 2 = Text string representing Outcome

Data Input for Interpretation
    The data is for a table with single columns of attributes
    Each row contains data from a case for interpretation

Array of Apriori Probabilities
    Probability for each outcome before attributes are known
    Single row, number of columns = number of outcomes
    Columns separated by spaces of tabs
    Values representing relative probabilities

Array of Costs
    Cost of wrongly missing an outcome
    Single row, number of columns = number of outcomes
    Columns separated by spaces of tabs
    Values representing relative costs

Program 1.

Reference Table of Counts
Input Table of Counts for Analysis of reference Table
    This is an alternative input for analysis
    The table is a count of the reference data
    Col 1 contains attributes of predictor
    The other columns are the outcomes
    The first row contains the outcome names
    Each following row represent attributes
    Each cell is the count of the attribute (row) for that outcome (Col)
Program 2.

Probabilities for each attribute given the outcome P(a|o)
Table of Probabilities
Input Table of Probability of attributes for each outcome P(a|o)
    This is a table of probabilities P(a|o)
    Col 1 contains attributes
    The other columns are the outcomes
    The first row contains the outcome names
    Each following row shows each attribute, and its P(a|o)
    Each cell is the Probability attribute for the outcome P(a|o)
Program 3.

Program 4.