2. Submit Predictions Statistics & Analysis Data Management Hypotheses Goal Get Data Predict whom survived the Titanic Disaster Score = Number of Passengers in Test Dataset Correctly Predict Passenger’s Fate

3. Training and Test Data Training Data N=891 39% Survived Test Data N=418 All Titanic Passengers N= 2,223 Develop Model How similar is the Test Data to the Training Data? If Similar, then model should do well. If Differenet, then model could perform poorly.

4. Kitchen Sink Over-Fitting?

5. Decision Tree Pruning model.6 <- rpart(survived ~ sex + age + pclass + sibsp + parch + fare + embarked, data = train_data, maxdepth=2)

6. Hold Out and Cross-Validation

7. Random Forest: Multiple Trees

8. Confusion Matrix 01%Err 02434315% 13812224% 44618% RandomForestGenderDecision Tree 01%Err 02503613% 15210833% 44620% 01%Err 02434315% 15210833% 44621% False Positives False Negatives

9. Model Ceiling 320 418 340 Gender Model Seems Realistic

10. survivedpclassNamesexagesibspparchticketFarecabinembarked 12Louch, Mrs. Charles Alexander (Alice Adelaide Slow)female4210 SC/AH 308526S 02Carter, Mrs. Ernest Courtenay (Lilian Hughes)female441024425226S 13Asplund, Miss. Lillian Gertrudfemale54234707731.3875S 03Andersson, Miss. Ebba Iris Alfridafemale64234708231.275S 11Bjornstrom-Steffansson, Mr. Mauritz Hakanmale280011056426.55C52S 01Long, Mr. Milton Clydemale290011350130D6S 11Simonius-Blumer, Col. Oberst Alfonsmale56001321335.5A26C 01Smith, Mr. James Clinchmale56001776430.6958A7C Why a Model Ceiling? Below are 4 pairs of passengers with very similar Predictor Variables; Yet, within each pair, one survived, and the other did not. At some point there just isn’t the data / variable to help make an accurate prediction.