... • 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
Text mining and image recognition.
Who is trying to extract meaningful information form these data?
Machine learning specialists
... 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) , ...
... 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 ...
... "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
... Desirable Properties of a Data Mining
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 ...
... equation for the marginal cost of a telephone call faced by various competing longdistance telephone carriers.
... 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 ...
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.
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
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 ...
... 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,
Multinomial logistic regression
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).Multinomial logistic regression is known by a variety of other names, including polytomous LR, multiclass LR, softmax regression, multinomial logit, maximum entropy (MaxEnt) classifier, conditional maximum entropy model.