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 ...

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 ...

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? ...

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 ...

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 ...

(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) , ...

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 ...

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. ...

Seeing the Light - Evolving Visually Guided Robots

... when the improvement from one step to the next is suitably small. Least square regression can be solved explicitly. ...

... when the improvement from one step to the next is suitably small. Least square regression can be solved explicitly. ...

Data Mining

... Desirable Properties of a Data Mining Method: Any nonlinear relationship between target and features can be approximated A method that works when the form of the nonlinearity is unknown The effect of interactions can be easily determined and incorporated into the model The method generalizes we ...

... Desirable Properties of a Data Mining Method: Any nonlinear relationship between target and features can be approximated A method that works when the form of the nonlinearity is unknown The effect of interactions can be easily determined and incorporated into the model The method generalizes we ...

Using Classification Tree Outcomes to Enhance Logistic Regression Models

... In the case of the tree above, I restricted the minimum number of cases that had to appear in each end node. The minimum number will be dependent upon the size of your sample dataset and what type of event is being modeled. In this case, requiring at least 40 cases to fall into each end node repres ...

... In the case of the tree above, I restricted the minimum number of cases that had to appear in each end node. The minimum number will be dependent upon the size of your sample dataset and what type of event is being modeled. In this case, requiring at least 40 cases to fall into each end node repres ...

Assignment 2

... equation for the marginal cost of a telephone call faced by various competing longdistance telephone carriers. ...

... equation for the marginal cost of a telephone call faced by various competing longdistance telephone carriers. ...

Predictive systems for computer-aided diagnosis in radiology

... ICH mortality • To assess the feasibility of Support Vector Machines in the selection of variables and creation of a prognostic ...

... ICH mortality • To assess the feasibility of Support Vector Machines in the selection of variables and creation of a prognostic ...

Student No

... varying? [hint: using plot(…, ylim=c(specify, specify))] C. for a new observation with X1 = 1, X2 = 1, X3=0.5, X4 = 0.5 and Z = 0, predict its Y ...

... varying? [hint: using plot(…, ylim=c(specify, specify))] C. for a new observation with X1 = 1, X2 = 1, X3=0.5, X4 = 0.5 and Z = 0, predict its Y ...

Data Mining Packages in R

... In addition to + and :, a number of other operators are useful in model formulae. The * operator denotes factor crossing: a*b interpreted as a+b+a:b. The ^ operator indicates crossing to the specified degree. For example (a+b+c)^2 is identical to (a+b+c)*(a+b+c) which in turn expands to a formula co ...

... In addition to + and :, a number of other operators are useful in model formulae. The * operator denotes factor crossing: a*b interpreted as a+b+a:b. The ^ operator indicates crossing to the specified degree. For example (a+b+c)^2 is identical to (a+b+c)*(a+b+c) which in turn expands to a formula co ...

New Scientific Data for Nowcasting and Forecasting Space Weather?

... A branch of statistics We use regression algorithms here Data laid out as for matrix inversion (little like finding best fit line with 2D data) Many algorithms (see [2] for an excellent introduction), some are like linear regression e.g. ...

... A branch of statistics We use regression algorithms here Data laid out as for matrix inversion (little like finding best fit line with 2D data) Many algorithms (see [2] for an excellent introduction), some are like linear regression e.g. ...

Data mining definition

... Data mining became a Computer Science subject in the last 10 years, but it will always use mathematics as the base of it. ...

... Data mining became a Computer Science subject in the last 10 years, but it will always use mathematics as the base of it. ...

Data Mining as Exploratory Data Analysis Summary Marginal Dependence Marginal Pairwise Dependence

... manner. Instead of estimating the parameters of a (restrictive) assumed parametric model and giving them a causal interpretation, potentially interesting patterns can be learned from the data using statistical learning algorithms. Exploratory data analysis using statistical learning can support futu ...

... manner. Instead of estimating the parameters of a (restrictive) assumed parametric model and giving them a causal interpretation, potentially interesting patterns can be learned from the data using statistical learning algorithms. Exploratory data analysis using statistical learning can support futu ...

Demographics and Behavioral Data Mining Case Study

... without doing any data processing • Random is 50% or .50. We are .737-.50 better than random by 23.7% ...

... without doing any data processing • Random is 50% or .50. We are .737-.50 better than random by 23.7% ...

Slides - clear - Rice University

... • Patterns, durations, frequencies and sequences of patterns defined by an anesthesiologist ...

... • Patterns, durations, frequencies and sequences of patterns defined by an anesthesiologist ...

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... M bits. If any M-‐bit classifier has error more than 2\epsilon fraction of points of the distribution, then the probability it has error only \epsilon fraction of training set is < 2-‐M . Hence ...

... M bits. If any M-‐bit classifier has error more than 2\epsilon fraction of points of the distribution, then the probability it has error only \epsilon fraction of training set is < 2-‐M . Hence ...

Computer lab 4: Linear classification methods

... associated p-values to examine which of the explanatory variables that seem to contribute the most to the classification of customers. b) Select a few subsets of your input variables and repeat the model fitting and estimation of misclassification rate. How does the predictive power vary with the su ...

... associated p-values to examine which of the explanatory variables that seem to contribute the most to the classification of customers. b) Select a few subsets of your input variables and repeat the model fitting and estimation of misclassification rate. How does the predictive power vary with the su ...

y mx b = +

... should then check to see if any of the n birthdays are identical. The function should perform this experiment at least 5000 times and calculate the fraction of those times in which two or more people had the same birthday.) Write a test program that calculates and prints out the probability that two ...

... should then check to see if any of the n birthdays are identical. The function should perform this experiment at least 5000 times and calculate the fraction of those times in which two or more people had the same birthday.) Write a test program that calculates and prints out the probability that two ...

mt11-req

... 174-181 (except line smoother), 186-197 (no regression trees). Moreover, I recommend to read the description of K-means, EM, and kNN in the “Top 10 data mining algorithms” article, posted on the webpage. Checklist: hypothesis class, VC-dimension, basic regression, overfitting, underfitting, training ...

... 174-181 (except line smoother), 186-197 (no regression trees). Moreover, I recommend to read the description of K-means, EM, and kNN in the “Top 10 data mining algorithms” article, posted on the webpage. Checklist: hypothesis class, VC-dimension, basic regression, overfitting, underfitting, training ...