Decision tree Nave Bayes Neural Nets Nearest Neighbor

אלגוריתמים סיווג (Decision tree) עץ החלטה Naïve Bayes Neural Nets Nearest Neighbor • •

Training / Testing

? איך מייצרים איך החלטות 1. Check for base cases and return these rules 2. For each attribute a a. Find the normalized information gain ratio from splitting on a 3. Let a_best be the attribute with the highest normalized information gain 4. Create a decision node that splits on a_best 5. Recurse on the sublists obtained by splitting on a_best, and add those nodes as children of node

ENTROPY הגדרות

? איך מייצרים איך החלטות 1. Check for base cases and return these rules 2. For each attribute a a. Find the normalized information gain ratio from splitting on a 3. Let a_best be the attribute with the highest normalized information gain 4. Create a decision node that splits on a_best 5. Recurse on the sublists obtained by splitting on a_best, and add those nodes as children of node

. . . איך עובד התהליך al c i r go e t ca eg t ca o al c i r s u uo in nt o c ss a l c Splitting Attributes -- Nodes Refund Yes No NO Mar. St Single, Divorced Tax. Inc < 80 K NO Training Data Married NO > 80 K YES Model: Decision Tree

? מה קובע אם יהיה לי עץ כזה al c ri ca go e t al c ri s u uo o eg t ca co in t n ss a cl Married Mar. St NO Single, Divorced Refund No Yes NO Tax. Inc < 80 K NO > 80 K YES There could be more than one tree that fits the same data!

איך קוראים את העץ IF -> then , אוסף של חוקי Test Data Start from the root of tree. Refund Yes No NO Mar. St Single, Divorced Tax. Inc < 80 K NO Married NO > 80 K YES

Apply Model to Test Data Refund Yes No NO Mar. St Single, Divorced Tax. Inc < 80 K NO Married NO > 80 K YES

Apply Model to Test Data Refund Yes No NO Mar. St Single, Divorced Tax. Inc < 80 K NO Married NO > 80 K YES Assign Cheat to “No”