Classification
... "close" as possible to one another, and different groups are as "far" as possible from one another, where distance is measured with respect to specific variable (s) we are trying to predict. ...
... "close" as possible to one another, and different groups are as "far" as possible from one another, where distance is measured with respect to specific variable (s) we are trying to predict. ...
BIS 541
... Part a and b are done by hand calculations a) Find all frequent itemsets using the Apriori algorithm b) List all strong association rules c) Find frequent intemsets and strong rules using RapidMiner start with the given minsuport and minconfidence Experiment with minsuport and miconfidence increasae ...
... Part a and b are done by hand calculations a) Find all frequent itemsets using the Apriori algorithm b) List all strong association rules c) Find frequent intemsets and strong rules using RapidMiner start with the given minsuport and minconfidence Experiment with minsuport and miconfidence increasae ...
(I) Predictive Analytics (II) Inferential Statistics and Prescriptive
... 3.Additive Models,Trees,and Boosting: Generalized additive models, Regression and classification trees , Boosting methods-exponential loss and AdaBoost, Numerical Optimization via gradient boosting ,Examples ( Spam data, California housing , NewZealand fish, Demographic data) 4.Neural Networks(NN) , ...
... 3.Additive Models,Trees,and Boosting: Generalized additive models, Regression and classification trees , Boosting methods-exponential loss and AdaBoost, Numerical Optimization via gradient boosting ,Examples ( Spam data, California housing , NewZealand fish, Demographic data) 4.Neural Networks(NN) , ...
Midterm Review
... Binary splits on any predictor X Best split found algorithmically by gini or entropy to maximize purity Best size can be found via cross validation Can be unstable ...
... Binary splits on any predictor X Best split found algorithmically by gini or entropy to maximize purity Best size can be found via cross validation Can be unstable ...
2.10 Random Forests for Scientific Discovery
... The Data Avalanche We can gather and store larger amounts of data than ever before: Satellite data Web data EPOS Microarrays etc Text mining and image recognition. Who is trying to extract meaningful information form these data? Academic statisticians Machine learning specialists ...
... The Data Avalanche We can gather and store larger amounts of data than ever before: Satellite data Web data EPOS Microarrays etc Text mining and image recognition. Who is trying to extract meaningful information form these data? Academic statisticians Machine learning specialists ...
LogReg178winter07
... • Fingerprints are matched against a data-base. • Each match is scored. • Using Logistic Regression we try to predict if a future match is a real or false. • Human fingerprint examiners claim 100% accuracy. Is this true? ...
... • Fingerprints are matched against a data-base. • Each match is scored. • Using Logistic Regression we try to predict if a future match is a real or false. • Human fingerprint examiners claim 100% accuracy. Is this true? ...
Searching for Patterns: Sean Early PSLC Summer School 2007
... knowledge component, and the number of opportunities that the student has had to respond correctly to that knowledge component ...
... knowledge component, and the number of opportunities that the student has had to respond correctly to that knowledge component ...
15.062 Data Mining – Spring 2003 Nitin R. Patel Multiple
... Robustness to Outliers in indep vars Robustness to irrelevant variables Ease of handling of missing values Natural handling both categorical and ...
... Robustness to Outliers in indep vars Robustness to irrelevant variables Ease of handling of missing values Natural handling both categorical and ...