411notes
... bottom left)). Such properties are refered to as noise. When this happens we say that the model does not generalize well to the test data. Rather it produces predictions on the test data that are much less accurate than you might have hoped for given the fit to the training data. Machine learning pr ...
... bottom left)). Such properties are refered to as noise. When this happens we say that the model does not generalize well to the test data. Rather it produces predictions on the test data that are much less accurate than you might have hoped for given the fit to the training data. Machine learning pr ...
The Contribution Of Data Mining
... relationships between objects, link analysis can find particularly important or well-connected objects and show where networks may be weak (e.g., in which all paths go through one or a small number of objects). According to Mitra et al. (2002) the main challenges in the data mining procedure are: ma ...
... relationships between objects, link analysis can find particularly important or well-connected objects and show where networks may be weak (e.g., in which all paths go through one or a small number of objects). According to Mitra et al. (2002) the main challenges in the data mining procedure are: ma ...
Predictive Modeling and Analysis of Student Academic Performance
... required to take (Ibrahim, 2004; Rubin & Altus, 2000; Zhu, Aung, & Zhou, 2010). The course cultivates students’ ability to “visualize the interactions of forces and moments, etc., with the physical world” (Muthu & Glass, 1999). It is an essential basis for many advanced engineering courses such as a ...
... required to take (Ibrahim, 2004; Rubin & Altus, 2000; Zhu, Aung, & Zhou, 2010). The course cultivates students’ ability to “visualize the interactions of forces and moments, etc., with the physical world” (Muthu & Glass, 1999). It is an essential basis for many advanced engineering courses such as a ...
Streaming Pattern Discovery in Multiple Time
... data can be viewed as a continuously growing t×n matrix Xt := [x1 x2 · · · xt ]T ∈ Rt×n , where one new row is added at each time tick t. In the chlorine example, xt is the measurements column-vector at t over all the sensors, where n is the number of chlorine sensors and t is the measurement timest ...
... data can be viewed as a continuously growing t×n matrix Xt := [x1 x2 · · · xt ]T ∈ Rt×n , where one new row is added at each time tick t. In the chlorine example, xt is the measurements column-vector at t over all the sensors, where n is the number of chlorine sensors and t is the measurement timest ...
WEKA Overview
... 1. After loading in the dataset, press “Choose” and select “Linear Regression” from the functions category. Configure the settings accordingly. 2. Second, select “Use training set.” This will create a linear regression model for the loaded data. 3. Third, press “Start.” This will now create a model ...
... 1. After loading in the dataset, press “Choose” and select “Linear Regression” from the functions category. Configure the settings accordingly. 2. Second, select “Use training set.” This will create a linear regression model for the loaded data. 3. Third, press “Start.” This will now create a model ...
Efficient Distributed Decision Trees for Robust - Infoscience
... Our paper aims to improve the robustness of distributed regression trees by preventing the outliers from influencing the tree induction phase based on robust loss functions. Above post-processing methods can be smoothly integrated into our framework. Distributed classification/regression trees: Our ...
... Our paper aims to improve the robustness of distributed regression trees by preventing the outliers from influencing the tree induction phase based on robust loss functions. Above post-processing methods can be smoothly integrated into our framework. Distributed classification/regression trees: Our ...
Feature Subset Selection and Feature Ranking for
... Consequently, to utilize these techniques on MTS data sets, each MTS item needs to be first transformed into one row or column vector. For example, in [9], where an EEG data set with 39 channels is used, an autoregressive (AR) model of order 3 [12] is utilized to represent each channel. Hence, each ...
... Consequently, to utilize these techniques on MTS data sets, each MTS item needs to be first transformed into one row or column vector. For example, in [9], where an EEG data set with 39 channels is used, an autoregressive (AR) model of order 3 [12] is utilized to represent each channel. Hence, each ...
Classification System for Mortgage Arrear Management
... as soon as possible, while keeping the current operational cost. Research problem We develop a classification model to predict the behaviour of the mortgage customers who were healthy in the last month but do not pay the debt at the beginning of the current month. One label with two possible values ...
... as soon as possible, while keeping the current operational cost. Research problem We develop a classification model to predict the behaviour of the mortgage customers who were healthy in the last month but do not pay the debt at the beginning of the current month. One label with two possible values ...
A Simple Constraint-Based Algorithm for Efficiently Mining
... We outline here a prototypical randomized controlled experiment (RCE); although variations certainly exist, they are not discussed. An RCE is performed with an explicitly defined population of units (e.g., patients with chest pain) in some explicitly defined context or set of contexts (e.g., current ...
... We outline here a prototypical randomized controlled experiment (RCE); although variations certainly exist, they are not discussed. An RCE is performed with an explicitly defined population of units (e.g., patients with chest pain) in some explicitly defined context or set of contexts (e.g., current ...
chapter 6 data mining
... It is common to have observations with missing values for one or more variables. The primary options for addressing missing data are: (1) discard observations with any missing values, (2) discard variable(s) with missing values, (3) fill-in missing entries with estimated values, or (4) apply a data ...
... It is common to have observations with missing values for one or more variables. The primary options for addressing missing data are: (1) discard observations with any missing values, (2) discard variable(s) with missing values, (3) fill-in missing entries with estimated values, or (4) apply a data ...
Predicting ICU Mortality Risk by Grouping Temporal Trends from a
... In this work, we adapt FSM to produce the temporal trends that are common in the dataset. Intuitively, similar patients undergo similar physiologic trajectories during their ICU stays. Compared to temporal pattern mining (e,g, motif mining in (McMillan et al. 2012) and temporal variation summarizati ...
... In this work, we adapt FSM to produce the temporal trends that are common in the dataset. Intuitively, similar patients undergo similar physiologic trajectories during their ICU stays. Compared to temporal pattern mining (e,g, motif mining in (McMillan et al. 2012) and temporal variation summarizati ...
The Utility of Clustering in Prediction Tasks
... the data to an operator as input (k-means for example) that gives an output of the same data but taking much fewer bits to represent it. This transformation tells us something interesting about the data and its structure which could be exploited to improve the predictive power. One potential way of ...
... the data to an operator as input (k-means for example) that gives an output of the same data but taking much fewer bits to represent it. This transformation tells us something interesting about the data and its structure which could be exploited to improve the predictive power. One potential way of ...
Customer churn prediction for an insurance company
... churning possibilities (predicted with logistic regression or neural networks) are ordered from high to low, and 20% of the customers with the highest churning possibility are contacted, it is expected from a cost-benefit analysis that no net costs are made. The neural network technique generates a ...
... churning possibilities (predicted with logistic regression or neural networks) are ordered from high to low, and 20% of the customers with the highest churning possibility are contacted, it is expected from a cost-benefit analysis that no net costs are made. The neural network technique generates a ...
Boosted Classification Trees and Class Probability/Quantile Estimation
... as exemplified by logistic regression. Given an estimate of the CCPF one can trivially achieve classification at any arbitrary quantile by thresholding the estimated CCPF at that quantile. If estimation of a CCPF solves the problem of classification for arbitrary costs, quantiles and imbalances, it ...
... as exemplified by logistic regression. Given an estimate of the CCPF one can trivially achieve classification at any arbitrary quantile by thresholding the estimated CCPF at that quantile. If estimation of a CCPF solves the problem of classification for arbitrary costs, quantiles and imbalances, it ...
Mining the FIRST Astronomical Survey Imola K. Fodor and Chandrika Kamath
... – find the largest (in abs value) coefficient V j , p , and discard the corresponding original variable X j – repeat the procedure w/ the second-to-last PC, and iterate until only 20 variables remain Call these PCA features ...
... – find the largest (in abs value) coefficient V j , p , and discard the corresponding original variable X j – repeat the procedure w/ the second-to-last PC, and iterate until only 20 variables remain Call these PCA features ...
associative regressive decision rule mining for predicting
... regression tree [12] to make prediction aiming at reducing the execution flow. However the prediction accuracy did not effectively increase. Many research works were conducted to answer top-k queries using Pareto Based Dominant Graph (DG) [10] aiming at improving the search efficiency. However, the ...
... regression tree [12] to make prediction aiming at reducing the execution flow. However the prediction accuracy did not effectively increase. Many research works were conducted to answer top-k queries using Pareto Based Dominant Graph (DG) [10] aiming at improving the search efficiency. However, the ...
Cycle-Time Key Factor Identification and
... In this paper, we predict WT using a version of the naı̈ve Bayesian classifier (NBC) [20], [21]. This is an accurate and efficient model that has many advantages over other models, especially when considering implementation and deployment in the fab. Since NBC is usually applied to discrete rather t ...
... In this paper, we predict WT using a version of the naı̈ve Bayesian classifier (NBC) [20], [21]. This is an accurate and efficient model that has many advantages over other models, especially when considering implementation and deployment in the fab. Since NBC is usually applied to discrete rather t ...
Supervised Discretization for Optimal Prediction
... cases of negative cases(2 and 4) in each example. The reason for these negative cases is the statistical uncertainty from the stepwise discretization scheme. These result also support the previous statement that τ-based discretization is more sensitive to the change of distribution since Case 3 is g ...
... cases of negative cases(2 and 4) in each example. The reason for these negative cases is the statistical uncertainty from the stepwise discretization scheme. These result also support the previous statement that τ-based discretization is more sensitive to the change of distribution since Case 3 is g ...
Collinearity: a review of methods to deal with it and a simulation
... Collinearity describes the situation where two or more predictor variables in a statistical model are linearly related (sometimes also called multicollinearity: Alin 2010). Many statistical routines, notably those most commonly used in ecology, are sensitive to collinearity (Belsley 1991, Chatfield ...
... Collinearity describes the situation where two or more predictor variables in a statistical model are linearly related (sometimes also called multicollinearity: Alin 2010). Many statistical routines, notably those most commonly used in ecology, are sensitive to collinearity (Belsley 1991, Chatfield ...
Multi-Dimensional Regression Analysis of Time
... time-series stream data, with the following contributions: (1) our analysis shows that only a small number of compressed regression measures instead of the complete stream of data need to be registered for multi-dimensional linear regression analysis, (2) to facilitate on-line stream data analysis, ...
... time-series stream data, with the following contributions: (1) our analysis shows that only a small number of compressed regression measures instead of the complete stream of data need to be registered for multi-dimensional linear regression analysis, (2) to facilitate on-line stream data analysis, ...
PPT - UCI
... • Model p( x | ck ) for each class and perform classification via Bayes rule, c = arg max { p( ck | x ) } = arg max { p( x | ck ) p(ck) } • How to model p( x | ck )? – p( x | ck ) = probability of a “bag of words” x given a class ck – Two commonly used approaches (for text): • Naïve Bayes: treat eac ...
... • Model p( x | ck ) for each class and perform classification via Bayes rule, c = arg max { p( ck | x ) } = arg max { p( x | ck ) p(ck) } • How to model p( x | ck )? – p( x | ck ) = probability of a “bag of words” x given a class ck – Two commonly used approaches (for text): • Naïve Bayes: treat eac ...
Ensemble Methods in Data Mining: Improving Accuracy
... Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges – from investment timing to drug discovery, and fraud detection to recommendation systems – where predictive accur ...
... Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges – from investment timing to drug discovery, and fraud detection to recommendation systems – where predictive accur ...
Data Mining Techniques for Mortality at Advanced Age
... detect interactions among variables and to decide the importance of a specific variable. Specific decision tree methods include Classification and Regression Trees (CART; Breiman et. al., 1984) and the count or Chi-squared Automatic Interaction Detection (CHAID; Kass, 1980) algorithm. CART and CHAID ...
... detect interactions among variables and to decide the importance of a specific variable. Specific decision tree methods include Classification and Regression Trees (CART; Breiman et. al., 1984) and the count or Chi-squared Automatic Interaction Detection (CHAID; Kass, 1980) algorithm. CART and CHAID ...
egerton university
... The data’s which are collected for the first time and those, which are original in character, is refer as primary data. There are several methods for primary data collections. Such methods include personal communication through interviews and personal observation. Secondary data The data’s that is a ...
... The data’s which are collected for the first time and those, which are original in character, is refer as primary data. There are several methods for primary data collections. Such methods include personal communication through interviews and personal observation. Secondary data The data’s that is a ...
Unilever Data Analysis Project - MIT Center for Digital Business
... through the Sloan School of Management Center for eBusiness. In this document we trace our interactions with Unilever, describe the data made available to us, describe various analyses and results, and present overall conclusions learned in the course of the project. Unilever has been a pioneer in m ...
... through the Sloan School of Management Center for eBusiness. In this document we trace our interactions with Unilever, describe the data made available to us, describe various analyses and results, and present overall conclusions learned in the course of the project. Unilever has been a pioneer in m ...