PPT - Department of Computer Science
... Ordonez, C., Zhao, K., A Comparison between Association Rules and Decision Trees to Predict Multiple Target Attributes, Intelligent Data Analysis (IDA), to appear in 2011. Navas, M., Ordonez, C., Baladandayuthapani, V., On the Computation of Stochastic Search Variable Selection in Linear Regression ...
... Ordonez, C., Zhao, K., A Comparison between Association Rules and Decision Trees to Predict Multiple Target Attributes, Intelligent Data Analysis (IDA), to appear in 2011. Navas, M., Ordonez, C., Baladandayuthapani, V., On the Computation of Stochastic Search Variable Selection in Linear Regression ...
Decision support system design for preventing icu readmission
... In this study, a new decision support system (DSS) is proposed for identifying patients at high risk of readmissions to intensive care units (ICU) within 24 to 72 hours after discharge. ICU readmission prediction is a complex problem, in which physiological variable selection, interactions of variab ...
... In this study, a new decision support system (DSS) is proposed for identifying patients at high risk of readmissions to intensive care units (ICU) within 24 to 72 hours after discharge. ICU readmission prediction is a complex problem, in which physiological variable selection, interactions of variab ...
Model selection in R featuring the lasso
... • Backwards elimination: Start with every candidate predictor in the model. Remove variables one at a time until all remaining variables are “significantly” associated with response. • Stepwise selection: As forward selection, but at each iteration remove variables which are made obsolete by new add ...
... • Backwards elimination: Start with every candidate predictor in the model. Remove variables one at a time until all remaining variables are “significantly” associated with response. • Stepwise selection: As forward selection, but at each iteration remove variables which are made obsolete by new add ...
Exact Age Prediction in Social Networks
... the average age in years that our predictions are off by, while R2 gives an indication of how much of the total variance is captured by the model. The results of this experiment are shown in Table 1. We see that using linear regression on DeepWalk features provides the best MAE until 95% of training ...
... the average age in years that our predictions are off by, while R2 gives an indication of how much of the total variance is captured by the model. The results of this experiment are shown in Table 1. We see that using linear regression on DeepWalk features provides the best MAE until 95% of training ...
Program Brochure - Indian Statistical Institute, Bangalore
... The participants will acquire the knowledge required for • Data exploration and visualization • Preprocessing and transformations • Supervised (predictive modeling) and unsupervised learning techniques • Interpretation and validation of results • Hands on experience on the usage of open source packa ...
... The participants will acquire the knowledge required for • Data exploration and visualization • Preprocessing and transformations • Supervised (predictive modeling) and unsupervised learning techniques • Interpretation and validation of results • Hands on experience on the usage of open source packa ...
Multiple Linear Regression in Data Mining
... predictions in that they minimize the expected squared error. In other words, amongst all linear models, as defined by equation (1) above, the model using the least squares estimates, β̂0 , β̂1 , β̂2 , . . . , β̂p , will give the smallest value of squared error on the average. We elaborate on this id ...
... predictions in that they minimize the expected squared error. In other words, amongst all linear models, as defined by equation (1) above, the model using the least squares estimates, β̂0 , β̂1 , β̂2 , . . . , β̂p , will give the smallest value of squared error on the average. We elaborate on this id ...
Research Methods for the Learning Sciences
... 2. IF the student spent at least 12 seconds reading the parasite information page AND the student read the parasite information page at least twice AND the student spent no more than 51 seconds reading the pesticides ...
... 2. IF the student spent at least 12 seconds reading the parasite information page AND the student read the parasite information page at least twice AND the student spent no more than 51 seconds reading the pesticides ...
Spark Cluster Computing with Working Sets Matei Zaharia, Mosharaf Chowdhury, Michael Franklin, Scott Shenker, Ion Stoica
... first iteration 174 s further iterations 6 s ...
... first iteration 174 s further iterations 6 s ...
pptx
... • An F test is fine, just include a student term (but note, your regressor itself should not predict using student as a variable… unless you want it to only work in your original population) ...
... • An F test is fine, just include a student term (but note, your regressor itself should not predict using student as a variable… unless you want it to only work in your original population) ...
Document
... the middle may not be strong. The cstatistic criterion requires a model to be able to rank order for the whole population. See the chart on the right. ...
... the middle may not be strong. The cstatistic criterion requires a model to be able to rank order for the whole population. See the chart on the right. ...
Finding the Gold in Your Data
... of people who buy B will result in 33% better sales than marketing to a random 30% of the people. ...
... of people who buy B will result in 33% better sales than marketing to a random 30% of the people. ...
The Comparisons of Data Mining Techniques for the Predictive
... methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the ...
... methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the ...
STAT 557 - Penn State Department of Statistics
... Pattern Recognition and Neural Networks by B. Ripley Prerequisites: STAT 511 or similar course, e.g. STAT415+STAT501 that covers analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression. ...
... Pattern Recognition and Neural Networks by B. Ripley Prerequisites: STAT 511 or similar course, e.g. STAT415+STAT501 that covers analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression. ...
BASUG_Data_Mining_Tutorial
... of people who buy B will result in 33% better sales than marketing to a random 30% of the people. ...
... of people who buy B will result in 33% better sales than marketing to a random 30% of the people. ...
Slide - chandan reddy
... • Commonly used models such as linear and logistic regression cannot handle nonnegative outcome variables and censoring directly. ...
... • Commonly used models such as linear and logistic regression cannot handle nonnegative outcome variables and censoring directly. ...
Predicting the Accuracy of Regression Models in the Retail Industry
... In early approaches to predict sales, a single model could be used for a whole business. As more detailed data becomes available, retail companies are dividing the problem into several sub-problems (predict the sales of each of its stores or product categories). The same trend can be observed in sev ...
... In early approaches to predict sales, a single model could be used for a whole business. As more detailed data becomes available, retail companies are dividing the problem into several sub-problems (predict the sales of each of its stores or product categories). The same trend can be observed in sev ...
CSC475 Music Information Retrieval
... simply searching for a hyperplane that perfectly separates the two classes we also have the additional requirement of a maximum margin. This can be cast as non-linear (quadratic) optimization problem subject to a set of linear inequality constraints. G. Tzanetakis ...
... simply searching for a hyperplane that perfectly separates the two classes we also have the additional requirement of a maximum margin. This can be cast as non-linear (quadratic) optimization problem subject to a set of linear inequality constraints. G. Tzanetakis ...
Automatic Classification of tissue samples
... 1. to investigate the possibility of automatically identifying abnormalities in tissue samples through the use of an ensemble model on data generated by histological examination 2. to minimize the number of false negative cases. ...
... 1. to investigate the possibility of automatically identifying abnormalities in tissue samples through the use of an ensemble model on data generated by histological examination 2. to minimize the number of false negative cases. ...
Using SAS® to Extend Logistic Regression
... The above procedure is used when outcome is dichotomous. It can also be extended to the cases of polychotomous outcome. We can define obesity as a binary variable, marked by BMI >=30 and <30, we can also define it as: obese (BMI >=30) overweight (25<=BMI<30) normal weight (18<=BMI<25) . Polychotomou ...
... The above procedure is used when outcome is dichotomous. It can also be extended to the cases of polychotomous outcome. We can define obesity as a binary variable, marked by BMI >=30 and <30, we can also define it as: obese (BMI >=30) overweight (25<=BMI<30) normal weight (18<=BMI<25) . Polychotomou ...
Logistic Regression - Brigham Young University
... evaluate the relationship between one variable (termed the dependent variable) and one or more other variables (termed the independent variables). It is a form of global analysis as it only produces a single equation for the relationship. • A model for predicting one variable from another. ...
... evaluate the relationship between one variable (termed the dependent variable) and one or more other variables (termed the independent variables). It is a form of global analysis as it only produces a single equation for the relationship. • A model for predicting one variable from another. ...
Profit-based Logistic Regression: A Case Study in Credit Card Fraud
... takes only two values and the independent variables are continuous, categorical, or both. The goal in LR is to find the best fitting, and most parsimonious model, to describe the relationship between a response or outcome variable, and a set of explanatory or predictor variables. LR model predicts t ...
... takes only two values and the independent variables are continuous, categorical, or both. The goal in LR is to find the best fitting, and most parsimonious model, to describe the relationship between a response or outcome variable, and a set of explanatory or predictor variables. LR model predicts t ...
Abstract The interest for data mining ... decades, due to its potential ...
... The interest for data mining models (DMM) has increased tremendously the past decades, due to its potential for uncovering valuable information hidden in massive data sets. There exist several categories of data mining tasks, such as e.g. clustering, regression, association analysis, etc., but this ...
... The interest for data mining models (DMM) has increased tremendously the past decades, due to its potential for uncovering valuable information hidden in massive data sets. There exist several categories of data mining tasks, such as e.g. clustering, regression, association analysis, etc., but this ...
Chapter 4 Describing the Relation Between Two Variables
... The following data are based on a study for drilling rock. The researchers wanted to determine whether the time it takes to dry drill a distance of 5 feet in rock increases with the depth at which the drilling begins. So, depth at which drilling begins is the predictor variable, x, and time (in minu ...
... The following data are based on a study for drilling rock. The researchers wanted to determine whether the time it takes to dry drill a distance of 5 feet in rock increases with the depth at which the drilling begins. So, depth at which drilling begins is the predictor variable, x, and time (in minu ...
data mining techniques for sales forecastings
... estimated from the data. Such models are called linear models.[8] B. Naive Bayes Classifirer A Naïve Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesianstatistics) with strong (naive) independence assumptions. A more descriptive term for the underlyi ...
... estimated from the data. Such models are called linear models.[8] B. Naive Bayes Classifirer A Naïve Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesianstatistics) with strong (naive) independence assumptions. A more descriptive term for the underlyi ...