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SEO report with information and free domain appraisal for feat.engineering. It is a domain hosted in . Its server is hosted on IP 206.189.58.26. The domain is ranked at the number 905227 as a world ranking of web pages. According to estimated data we have access to potential gains of this site are 59 dollars per month. In annual terms, the domain feat.engineering could be earning more than 713 dollars. If someone wanted to buy the domain a good selling price would be 7133 dollars.
These data are estimated and can not be taken as 100 % real. Only give an estimate of profits depending on the subject and the traffic generated by feat.engineering . The estimated number of unique visitors accessing feat.engineering is 519. Thus the total number of people who visit this page per month would be 15582 people.
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- Current Ip of the domain: 206.189.58.26
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- Total Links: Anchor textTypeLinkPrefacefollowindex.html1 Introductionfollowintro-intro.html1.1 A Simple Examplefollowa-simple-example.html1.2 Important Conceptsfollowimportant-concepts.html1.2.1 Overfittingfollowimportant-concepts.html#overfitting1.2.2 Supervised and Unsupervised Proceduresfollowimportant-concepts.html#supervised-and-unsupervised-procedures1.2.3 No Free Lunchfollowimportant-concepts.html#no-free-lunch1.2.4 The Model versus the Modeling Processfollowimportant-concepts.html#the-model-versus-the-modeling-process1.2.5 Model Bias and Variancefollowimportant-concepts.html#model-bias-and-variance1.2.6 Experience-Driven Modeling and Empirically Driven Modelingfollowimportant-concepts.html#experience-driven-modeling-and-empirically-driven-modeling1.2.7 Big Datafollowimportant-concepts.html#big-data1.3 A More Complex Examplefollowa-more-complex-example.html1.4 Feature Selectionfollowfeature-selection.html1.5 An Outline of the Bookfollowan-outline-of-the-book.html1.6 Computingfollowintro-computing.html2 Illustrative Example: Predicting Risk of Ischemic Strokefollowstroke-tour.html2.1 Splittingfollowsplitting.html2.2 Preprocessingfollowstroke-preprocessing.html2.3 Explorationfollowstroke-exploration.html2.4 Predictive Modeling Across Setsfollowpredictive-modeling-across-sets.html2.5 Other Considerationsfollowother-considerations.html2.6 Computingfollowstoke-computing.html3 A Review of the Predictive Modeling Processfollowreview-predictive-modeling-process.html3.1 Illustrative Example: OkCupid Profile Datafollowokc-intro.html3.2 Measuring Performancefollowmeasuring-performance.html3.2.1 Regression Metricsfollowmeasuring-performance.html#reg-metrics3.2.2 Classification Metricsfollowmeasuring-performance.html#class-metrics3.2.3 Context-Specific Metricsfollowmeasuring-performance.html#context-specific-metrics3.3 Data Splittingfollowdata-splitting.html3.4 Resamplingfollowresampling.html3.4.1 V-Fold Cross-Validation and Its Variantsfollowresampling.html#cv3.4.2 Monte Carlo Cross-Validationfollowresampling.html#monte-carlo-cross-validation3.4.3 The Bootstrapfollowresampling.html#the-bootstrap3.4.4 Rolling Origin Forecastingfollowresampling.html#rolling-origin-forecasting3.4.5 Validation Setsfollowresampling.html#validation-sets3.4.6 Variance and Bias in Resamplingfollowresampling.html#resample-var-bias3.4.7 What Should Be Included Inside of Resampling?followresampling.html#inside-resampling3.5 Tuning Parameters and Overfittingfollowtuning.html3.6 Model Optimization and Tuningfollowmodel-optimization-and-tuning.html3.7 Comparing Models Using the Training Setfollowreview-model-comparisons.html3.8 Feature Engineering Without Overfittingfollowfeature-engineering-without-overfitting.html3.9 Summaryfollowsummary.html3.10 Computingfollowreview-computing.html4 Exploratory Visualizationsfollowexploratory-visualizations.html4.1 Introduction to the Chicago Train Ridership Datafollowchicago-intro.html4.2 Visualizations for Numeric Data: Exploring Train Ridership Datafollowvisualizations-for-numeric-data-exploring-train-ridership-data.html4.2.1 Box Plots, Violin Plots, and Histogramsfollowvisualizations-for-numeric-data-exploring-train-ridership-data.html#box-plots-violin-plots-and-histograms4.2.2 Augmenting Visualizations through Faceting, Colors, and Shapesfollowvisualizations-for-numeric-data-exploring-train-ridership-data.html#augmenting-visualizations-through-faceting-colors-and-shapes4.2.3 Scatter Plotsfollowvisualizations-for-numeric-data-exploring-train-ridership-data.html#scatter-plots4.2.4 Heatmapsfollowvisualizations-for-numeric-data-exploring-train-ridership-data.html#heatmaps4.2.5 Correlation Matrix Plotsfollowvisualizations-for-numeric-data-exploring-train-ridership-data.html#correlation-matrix-plots4.2.6 Line Plotsfollowvisualizations-for-numeric-data-exploring-train-ridership-data.html#line-plots4.2.7 Principal Components Analysisfollowvisualizations-for-numeric-data-exploring-train-ridership-data.html#eda-pca4.3 Visualizations for Categorical Data: Exploring the OkCupid Datafollowvisualizations-for-categorical-data-exploring-the-okcupid-data.html4.3.1 Visualizing Relationships between Outcomes and Predictorsfollowvisualizations-for-categorical-data-exploring-the-okcupid-data.html#visualizing-relationships-between-outcomes-and-predictors4.3.2 Exploring Relationships Between Categorical Predictorsfollowvisualizations-for-categorical-data-exploring-the-okcupid-data.html#exploring-relationships-between-categorical-predictors4.4 Post Modeling Exploratory Visualizationsfollowpost-modeling.html4.5 Summaryfollowsummary-1.html4.6 Computingfolloweda-computing.html5 Encoding Categorical Predictorsfollowencoding-categorical-predictors.html5.1 Creating Dummy Variables for Unordered Categoriesfollowcreating-dummy-variables-for-unordered-categories.html5.2 Encoding Predictors with Many Categoriesfollowencoding-predictors-with-many-categories.html5.3 Approaches for Novel Categoriesfollowapproaches-for-novel-categories.html5.4 Supervised Encoding Methodsfollowcategorical-supervised-encoding.html5.5 Encodings for Ordered Datafollowencodings-for-ordered-data.html5.6 Creating Features from Text Datafollowtext-data.html5.7 Factors versus Dummy Variables in Tree-Based Modelsfollowcategorical-trees.html5.8 Summaryfollowsummary-2.html5.9 Computingfollowcategorical-computing.html6 Engineering Numeric Predictorsfollowengineering-numeric-predictors.html6.1 1:1 Transformationsfollownumeric-one-to-one.html6.2 1:Many Transformationsfollownumeric-one-to-many.html6.2.1 Nonlinear Features via Basis Expansions and Splinesfollownumeric-one-to-many.html#numeric-basis-functions6.2.2 Discretize Predictors as a Last Resortfollownumeric-one-to-many.html#binning6.3 Many:Many Transformationsfollownumeric-many-to-many.html6.3.1 Linear Projection Methodsfollownumeric-many-to-many.html#linear-projection-methods6.3.2 Autoencodersfollownumeric-many-to-many.html#autoencoders6.3.3 Spatial Signfollownumeric-many-to-many.html#spatial-sign6.3.4 Distance and Depth Featuresfollownumeric-many-to-many.html#distance-and-depth-features6.4 Summaryfollowsummary-3.html6.5 Computingfollownumeric-computing.html7 Detecting Interaction Effectsfollowdetecting-interaction-effects.html7.1 Guiding Principles in the Search for Interactionsfollowinteractions-guiding-principles.html7.2 Practical Considerationsfollowpractical-considerations.html7.3 The Brute-Force Approach to Identifying Predictive Interactionsfollowcomplete-enumeration.html7.3.1 Simple Screeningfollowcomplete-enumeration.html#complete-enumeration-simple-screening7.3.2 Penalized Regressionfollowcomplete-enumeration.html#penalized-regression7.4 Approaches when Complete Enumeration is Practically Impossiblefollowapproaches-when-complete-enumeration-is-practically-impossible.html7.4.1 Guiding Principles and Two-stage Modelingfollowapproaches-when-complete-enumeration-is-practically-impossible.html#guiding-principles-and-two-stage-modeling7.4.2 Tree-based Methodsfollowapproaches-when-complete-enumeration-is-practically-impossible.html#tree-based-methods7.4.3 The Feasible Solution Algorithmfollowapproaches-when-complete-enumeration-is-practically-impossible.html#the-feasible-solution-algorithm7.5 Other Potentially Useful Toolsfollowother-potentially-useful-tools.html7.6 Summaryfollowsummary-4.html7.7 Computingfollowinteraction-computing.html8 Handling Missing Datafollowhandling-missing-data.html8.1 Understanding the Nature and Severity of Missing Informationfollowunderstanding-the-nature-and-severity-of-missing-information.html8.2 Models that are Resistant to Missing Valuesfollowmodels-that-are-resistant-to-missing-values.html8.3 Deletion of Datafollowdeletion-of-data.html8.4 Encoding Missingnessfollowencoding-missingness.html8.5 Imputation methodsfollowimputation-methods.html8.6 Special Casesfollowspecial-cases.html8.7 Summaryfollowsummary-5.html8.8 Computingfollowmissing-computing.html9 Working with Profile Datafollowprofile-data.html9.1 Illustrative Data: Pharmaceutical Manufacturing Monitoringfollowillustrative-data-pharmaceutical-manufacturing-monitoring.html9.2 What are the Experimental Unit and the Unit of Prediction?followwhat-are-the-experimental-unit-and-the-unit-of-prediction.html9.3 Reducing Backgroundfollowreducing-background.html9.4 Reducing Other Noisefollowreducing-other-noise.html9.5 Exploiting Correlationfollowexploiting-correlation.html9.6 Impacts of Data Processing on Modelingfollowimpacts-of-data-processing-on-modeling.html9.7 Summaryfollowsummary-6.html9.8 Computingfollowprofile-computing.html10 Feature Selection Overviewfollowselection.html10.1 Goals of Feature Selectionfollowgoals-of-feature-selection.html10.2 Classes of Feature Selection Methodologiesfollowclasses-of-feature-selection-methodologies.html10.3 Effect of Irrelevant Featuresfollowfeature-selection-simulation.html10.4 Overfitting to Predictors and External Validationfollowselection-overfitting.html10.5 A Case Studyfollowa-case-study.html10.6 Next Stepsfollownext-steps.html10.7 Computingfollowselection-computing.html11 Greedy Search Methodsfollowgreedy-search.html11.1 Illustrative Data: Predicting Parkinson’s Diseasefollowillustrative-data-predicting-parkinsons-disease.html11.2 Simple Filtersfollowgreedy-simple-filters.html11.2.1 Simple Filters Applied to the Parkinson’s Disease Datafollowgreedy-simple-filters.html#simple-filters-applied-to-the-parkinsons-disease-data11.3 Recursive Feature Eliminationfollowrecursive-feature-elimination.html11.4 Stepwise Selectionfollowgreedy-stepwise-selection.html11.5 Summaryfollowsummary-7.html11.6 Computingfollowgreedy-computing.html12 Global Search Methodsfollowglobal.html12.1 Naive Bayes Modelsfollownaive-bayes.html12.2 Simulated Annealingfollowsimulated-annealing.html12.2.1 Selecting Features without Overfittingfollowsimulated-annealing.html#selecting-features-without-overfitting12.2.2 Application to Modeling the OkCupid Datafollowsimulated-annealing.html#application-to-modeling-the-okcupid-data12.2.3 Examining Changes in Performancefollowsimulated-annealing.html#examining-changes-in-performance12.2.4 Grouped Qualitative Predictors Versus Indicator Variablesfollowsimulated-annealing.html#grouped-qualitative-predictors-versus-indicator-variables12.2.5 The Effect of the Initial Subsetfollowsimulated-annealing.html#the-effect-of-the-initial-subset12.3 Genetic Algorithmsfollowgenetic-algorithms.html12.3.1 External Validationfollowgenetic-algorithms.html#external-validation12.3.2 Coercing Sparsityfollowgenetic-algorithms.html#coercing-sparsity12.4 Test Set Resultsfollowtest-set-results.html12.5 Summaryfollowsummary-8.html12.6 Computingfollowglobal-computing.htmlReferencesfollowreferences.htmlErrata and Version Historyfollowerrata-and-version-history.htmlFeature Engineering and Selection: A Practical Approach for Predictive Modelsfollow./Erratafollowhttps://bookdown.org/max/FES/errata-and-version-history.htmlAmazonfollowhttps://www.amazon.com/gp/product/1138079227/ref=as_li_tl?ie=UTF8&tag=apm0a-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=1138079227&linkId=c801e78acfc3bc022dbed02af4851962Taylor & Francisfollowhttps://www.crcpress.com/Feature-Engineering-and-Selection-A-Practical-Approach-for-Predictive-Models/Kuhn-Johnson/p/book/97811380792292010followreferences.html#ref-Raimondi20102016followreferences.html#ref-jahani2016comparison2016followreferences.html#ref-luo2016automatically2015followreferences.html#ref-stankovic2015investment2011followreferences.html#ref-thomson2011not1follow#fn12follow#fn23follow#fn3https://github.com/topepo/FESfollowhttps://github.com/topepo/FEShttps://bookdown.org/max/FESfollowhttps://bookdown.org/max/FES@alexpghayesfollowhttps://github.com/alexpghayes@AllardJMfollowhttps://github.com/AllardJM@AndrewKostandyfollowhttps://github.com/AndrewKostandy@bashhwufollowhttps://github.com/bashhwu@btloisfollowhttps://github.com/btlois@cdr6934followhttps://github.com/cdr6934@danielwofollowhttps://github.com/danielwo@davftfollowhttps://github.com/davft@drabenfollowhttps://github.com/draben@eddelbuettelfollowhttps://github.com/eddelbuettel@endorefollowhttps://github.com/endore@feinmannfollowhttps://github.com/feinmann@gteseifollowhttps://github.com/gtesei@ifellowsfollowhttps://github.com/ifellows@JohnMountfollowhttps://github.com/JohnMount@jonimatixfollowhttps://github.com/jonimatix@jrfiedlerfollowhttps://github.com/jrfiedler@juliasilgefollowhttps://github.com/juliasilge@jwillagefollowhttps://github.com/jwillage@kaliszpfollowhttps://github.com/kaliszp@KevinBretonnelCohenfollowhttps://github.com/KevinBretonnelCohen@kieroneilfollowhttps://github.com/kieroneil@KnightAdzfollowhttps://github.com/KnightAdz@kransom14followhttps://github.com/kransom14@LG-1followhttps://github.com/LG-1@LluisRamonfollowhttps://github.com/LluisRamon@LoweCoryrfollowhttps://github.com/LoweCoryr@lpatrunofollowhttps://github.com/lpatruno@mlduartefollowhttps://github.com/mlduarte@monogeneafollowhttps://github.com/monogenea@mpettisfollowhttps://github.com/mpettis@Nathan-Furnalfollowhttps://github.com/Nathan-Furnal@nazarenofollowhttps://github.com/nazareno@PedramNavidfollowhttps://github.com/PedramNavid@r0f1followhttps://github.com/r0f1@Ronen4321followhttps://github.com/Ronen4321@shinhongwufollowhttps://github.com/shinhongwu@stecaronfollowhttps://github.com/stecaron@StefanZaaimanfollowhttps://github.com/StefanZaaiman@treyspfollowhttps://github.com/treysp@uwesterrfollowhttps://github.com/uwesterr@van1991followhttps://github.com/van1991https://twitter.com/hashtag/rstats?src=hashfollowhttps://twitter.com/hashtag/rstats?src=hash↩followindex.html#fnref1https://stackoverflow.com/questions/tagged/rfollowhttps://stackoverflow.com/questions/tagged/r↩followindex.html#fnref2https://community.rstudio.com/followhttps://community.rstudio.com/↩followindex.html#fnref3followintro-intro.html
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- Estimated worth: 7133 dollars
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Feature Engineering and Selection: A Practical Approach for Predictive Models
A primary goal of predictive modeling is to find a reliable and effective predic- tive relationship between an available set of features and an outcome. This book provides an extensive set of techniques for uncovering effective representations of the fea
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