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StatsToDo : Classification by Basic Bayes Probability Program
Program Help & Hints
 Data: Attributes ± Outcome Designation 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 = Series of + (Positive) and - (Negative) for attributes     Col 2 = Single character or word for Outcome Designation Data Input for Interpretation     The data is for a table with single columns     Each row contains data from a case from the input data for interpretation     Column with series of + (Positive) and - (Negative) for attributes
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 attribute clusters     The other columns are the outcomes     The first row contains the outcome names     Each following row represent a set of attributes     Each cell is the count of the attribute (row) for that outcome (Col)
Program 2.

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

Program 4.