├── .DS_Store ├── .RData ├── .gitignore ├── 01-Introduction.Rmd ├── 02-SoftSkills.Rmd ├── 03-Dataset.Rmd ├── 04-BigDataCloudPlatform.Rmd ├── 05-DataPreprocessing.Rmd ├── 06-DataWrangling.Rmd ├── 07-ModelTuning.Rmd ├── 08-MeasurePerformance.Rmd ├── 09-Regression.Rmd ├── 10-Regularization.Rmd ├── 11-tree.Rmd ├── 12-DeepLearning.Rmd ├── 19-Appendix.Rmd ├── 20-Reference.Rmd ├── BackUp ├── .DS_Store ├── 08-FeatureEngineering.Rmd ├── 12-nnet.Rmd ├── 14-ReproducibleReport.Rmd ├── MeasurementError.Rmd ├── gd_algorithm.Rmd └── gd_algorithm.html ├── CONDUCT.md ├── Data ├── .DS_Store ├── AirlineRating.csv ├── ResponseError.csv ├── SegData.csv ├── VariableError.csv ├── avnnetTune.RData ├── divisions.csv ├── gbmTuneNN.Rdata ├── nnetTune.RData ├── segmentdb.csv ├── segmentdb_server.csv ├── segmentdb_ui.csv ├── sim1_da1.csv ├── sim1_da2.csv ├── sim1_da3.csv ├── tree_sample.csv └── wikiview.csv ├── IDS.Rproj ├── IDS.log ├── LICENSE ├── Python ├── .DS_Store ├── CNN.ipynb ├── DataPreprocessing.ipynb ├── DataWrangling.ipynb ├── FFNN.ipynb ├── LoadDatasetSpark.ipynb ├── MINST_with_one_hidden_layer.ipynb ├── Measuring_Performance.ipynb ├── Model_Tuning_Strategy.ipynb ├── PysparkETL.ipynb ├── RNN.ipynb ├── Regression_Models.ipynb ├── Regularization_Methods.ipynb ├── TokenizingPadding.ipynb └── TreeBasedModels.ipynb ├── R ├── GenerateTreePlots.R └── TreeBasedModel.Rmd ├── README.md ├── _bookdown.yml ├── _bookdown_files └── IDS_files │ ├── .DS_Store │ ├── figure-html │ ├── activationleakyrelu-1.svg │ ├── activationrelu-1.svg │ ├── activationsigmoid-1.svg │ ├── activationtanh-1.svg │ ├── corplotlm-1.svg │ ├── lassoregtune-1.svg │ ├── linearbias-1.svg │ ├── linearvar-1.svg │ ├── lmdiagnostic-1.svg │ ├── maxdis-1.svg │ ├── nnet_simulate_data-1.svg │ ├── preprocessskew-1.svg │ ├── ridgeregtune-1.svg │ ├── simpleedge-1.svg │ ├── times-1.svg │ ├── unnamed-chunk-11-1.svg │ ├── unnamed-chunk-110-1.svg │ ├── unnamed-chunk-112-1.svg │ ├── unnamed-chunk-114-1.svg │ ├── unnamed-chunk-115-1.svg │ ├── unnamed-chunk-116-1.svg │ ├── unnamed-chunk-118-1.svg │ ├── unnamed-chunk-119-1.svg │ ├── unnamed-chunk-121-1.svg │ ├── unnamed-chunk-123-1.svg │ ├── unnamed-chunk-125-1.svg │ ├── unnamed-chunk-126-1.svg │ ├── unnamed-chunk-128-1.svg │ ├── unnamed-chunk-13-1.svg │ ├── unnamed-chunk-130-1.svg │ ├── unnamed-chunk-132-1.svg │ ├── unnamed-chunk-133-1.svg │ ├── unnamed-chunk-134-1.svg │ ├── unnamed-chunk-135-1.svg │ ├── unnamed-chunk-137-1.svg │ ├── unnamed-chunk-138-1.svg │ ├── unnamed-chunk-139-1.svg │ ├── unnamed-chunk-14-1.svg │ ├── unnamed-chunk-140-1.svg │ ├── unnamed-chunk-141-1.svg │ ├── unnamed-chunk-142-1.svg │ ├── unnamed-chunk-144-1.svg │ ├── unnamed-chunk-146-1.svg │ ├── unnamed-chunk-147-1.svg │ ├── unnamed-chunk-149-1.svg │ ├── unnamed-chunk-150-1.svg │ ├── unnamed-chunk-152-1.svg │ ├── unnamed-chunk-153-1.svg │ ├── unnamed-chunk-154-1.svg │ ├── unnamed-chunk-155-1.svg │ ├── unnamed-chunk-157-1.svg │ ├── unnamed-chunk-16-1.svg │ ├── unnamed-chunk-17-1.svg │ ├── unnamed-chunk-18-1.svg │ ├── unnamed-chunk-19-1.svg │ ├── unnamed-chunk-20-1.svg │ ├── unnamed-chunk-46-1.svg │ ├── unnamed-chunk-47-1.svg │ ├── unnamed-chunk-48-1.svg │ ├── unnamed-chunk-49-1.svg │ ├── unnamed-chunk-70-1.svg │ ├── unnamed-chunk-72-1.svg │ ├── unnamed-chunk-73-1.svg │ ├── unnamed-chunk-75-1.svg │ ├── unnamed-chunk-76-1.svg │ ├── unnamed-chunk-78-1.svg │ ├── unnamed-chunk-80-1.svg │ ├── unnamed-chunk-81-1.svg │ ├── unnamed-chunk-82-1.svg │ ├── unnamed-chunk-90-1.svg │ ├── unnamed-chunk-92-1.svg │ ├── unnamed-chunk-94-1.svg │ ├── unnamed-chunk-95-1.svg │ ├── unnamed-chunk-96-1.svg │ ├── unnamed-chunk-97-1.svg │ └── weightedavgplot-1.svg │ └── figure-latex │ ├── activationleakyrelu-1.pdf │ ├── activationrelu-1.pdf │ ├── activationsigmoid-1.pdf │ ├── activationtanh-1.pdf │ ├── corplotlm-1.pdf │ ├── lassoregtune-1.pdf │ ├── linearbias-1.pdf │ ├── linearvar-1.pdf │ ├── lmdiagnostic-1.pdf │ ├── maxdis-1.pdf │ ├── nnet_simulate_data-1.pdf │ ├── preprocessskew-1.pdf │ ├── ridgeregtune-1.pdf │ ├── simpleedge-1.pdf │ ├── times-1.pdf │ ├── unnamed-chunk-105-1.pdf │ ├── unnamed-chunk-107-1.pdf │ ├── unnamed-chunk-114-1.pdf │ ├── unnamed-chunk-115-1.pdf │ ├── unnamed-chunk-116-1.pdf │ ├── unnamed-chunk-117-1.pdf │ ├── unnamed-chunk-118-1.pdf │ ├── unnamed-chunk-119-1.pdf │ ├── unnamed-chunk-120-1.pdf │ ├── unnamed-chunk-121-1.pdf │ ├── unnamed-chunk-125-1.pdf │ ├── unnamed-chunk-126-1.pdf │ ├── unnamed-chunk-127-1.pdf │ ├── unnamed-chunk-128-1.pdf │ ├── unnamed-chunk-129-1.pdf │ ├── unnamed-chunk-13-1.pdf │ ├── unnamed-chunk-132-1.pdf │ ├── unnamed-chunk-133-1.pdf │ ├── unnamed-chunk-134-1.pdf │ ├── unnamed-chunk-135-1.pdf │ ├── unnamed-chunk-136-1.pdf │ ├── unnamed-chunk-137-1.pdf │ ├── unnamed-chunk-138-1.pdf │ ├── unnamed-chunk-139-1.pdf │ ├── unnamed-chunk-14-1.pdf │ ├── unnamed-chunk-140-1.pdf │ ├── unnamed-chunk-141-1.pdf │ ├── unnamed-chunk-142-1.pdf │ ├── unnamed-chunk-143-1.pdf │ ├── unnamed-chunk-144-1.pdf │ ├── unnamed-chunk-145-1.pdf │ ├── unnamed-chunk-146-1.pdf │ ├── unnamed-chunk-148-1.pdf │ ├── unnamed-chunk-15-1.pdf │ ├── unnamed-chunk-151-1.pdf │ ├── unnamed-chunk-152-1.pdf │ ├── unnamed-chunk-153-1.pdf │ ├── unnamed-chunk-154-1.pdf │ ├── unnamed-chunk-155-1.pdf │ ├── unnamed-chunk-156-1.pdf │ ├── unnamed-chunk-157-1.pdf │ ├── unnamed-chunk-159-1.pdf │ ├── unnamed-chunk-16-1.pdf │ ├── unnamed-chunk-163-1.pdf │ ├── unnamed-chunk-166-1.pdf │ ├── unnamed-chunk-17-1.pdf │ ├── unnamed-chunk-18-1.pdf │ ├── unnamed-chunk-19-1.pdf │ ├── unnamed-chunk-20-1.pdf │ ├── unnamed-chunk-21-1.pdf │ ├── unnamed-chunk-23-1.pdf │ ├── unnamed-chunk-26-1.pdf │ ├── unnamed-chunk-28-1.pdf │ ├── unnamed-chunk-29-1.pdf │ ├── unnamed-chunk-48-1.pdf │ ├── unnamed-chunk-49-1.pdf │ ├── unnamed-chunk-50-1.pdf │ ├── unnamed-chunk-51-1.pdf │ ├── unnamed-chunk-58-1.pdf │ ├── unnamed-chunk-59-1.pdf │ ├── unnamed-chunk-72-1.pdf │ ├── unnamed-chunk-73-1.pdf │ ├── unnamed-chunk-74-1.pdf │ ├── unnamed-chunk-75-1.pdf │ ├── unnamed-chunk-76-1.pdf │ ├── unnamed-chunk-77-1.pdf │ ├── unnamed-chunk-78-1.pdf │ ├── unnamed-chunk-81-1.pdf │ ├── unnamed-chunk-82-1.pdf │ ├── unnamed-chunk-83-1.pdf │ ├── unnamed-chunk-84-1.pdf │ ├── unnamed-chunk-86-1.pdf │ ├── unnamed-chunk-92-1.pdf │ ├── unnamed-chunk-94-1.pdf │ ├── unnamed-chunk-95-1.pdf │ ├── unnamed-chunk-96-1.pdf │ ├── unnamed-chunk-97-1.pdf │ ├── unnamed-chunk-98-1.pdf │ ├── unnamed-chunk-99-1.pdf │ └── weightedavgplot-1.pdf ├── _output.yml ├── advances-in-computational-mathematics.csl ├── bibliography.bib ├── css ├── style.css └── toc.css ├── images ├── .DS_Store ├── AlogrithmTypes.png ├── AlogrithmTypes.xmind ├── BinaryTree.png ├── ConvOverVolume.png ├── DataPre-processing.png ├── DataPre-processing.xmind ├── DataScienceQuestion.png ├── DataScienceQuestions.xmind ├── DataScienceTimeline.png ├── DataScienceTimeline.xmind ├── GeneralProcessEN.png ├── GernalProcessEN.xmind ├── GradientDescent.png ├── InfoGainEN.png ├── LassoRidge.png ├── LearningStyles.png ├── LearningStyles.xmind ├── LearningStyles2.png ├── MnistExamples.png ├── ModelError.png ├── ModelError.xmind ├── ParameterTuningProcess.png ├── ParameterTuningProcess.xmind ├── RNN_pro.png ├── SkillEN.png ├── TokenizingPadding.png ├── cluster.png ├── cnn_history.png ├── cnn_summary.png ├── colabicon.png ├── colorimage.png ├── convlayer.png ├── convolution1.png ├── convolutionsbs.png ├── cv5fold.png ├── cv5fold.xmind ├── databases.png ├── datable1.png ├── dbxmarkdown.png ├── dbxrnotebook.png ├── decisionregion.png ├── dnn0.png ├── dnn0_fb3.png ├── dnn1.png ├── dnn_history.png ├── dnn_str.png ├── dsrole.png ├── edgedet1.jpg ├── edgedet2.png ├── edgedet3.jpg ├── edgedet4.jpg ├── edgedet5.png ├── eggshellconv.png ├── giniEN.png ├── grayscaleimage.png ├── infogainratioexp.png ├── infogainratioexp2.png ├── loadfromgithub.png ├── lstm1.png ├── lstm2.png ├── lstm3.png ├── lstm4.png ├── misclassified_img.png ├── misclassified_img_cnn.png ├── mnist_image3.png ├── nn_ohe.png ├── one_hot_encoding.png ├── onelayerNN.png ├── padding.png ├── poolinglayer.png ├── preface_coverage.png ├── prograssbar.png ├── questiontype.xmind ├── rSQL.png ├── rnn_embedding1.png ├── rnn_notation.png ├── rnnapplication.png ├── rnnhandson1.png ├── rnnhandson2.png ├── rnnrollout.png ├── rnnstrs.png ├── rnnunit.png ├── softskill1.png ├── softskill2.png ├── threetracks.png ├── track1_engineering.png ├── track2_analysis.png ├── track3_modeling.png ├── treeEN.png ├── treeEN.xmind ├── varEN.png ├── varEN.xmind ├── varEN2.png ├── varEN2.xmind └── weighted_agv.png ├── index.rmd ├── krantz.cls └── latex ├── .DS_Store ├── after_body.tex ├── before_body.tex └── preamble.tex /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/happyrabbit/IntroDataScience/85dd2eaf16ca4e43968e30bdf963b2f5dd26bd32/.DS_Store -------------------------------------------------------------------------------- /.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/happyrabbit/IntroDataScience/85dd2eaf16ca4e43968e30bdf963b2f5dd26bd32/.RData -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .httr-oauth 2 | .Rproj.user 3 | .Rhistory 4 | .RData 5 | -------------------------------------------------------------------------------- /20-Reference.Rmd: -------------------------------------------------------------------------------- 1 | `r if (knitr:::is_html_output()) ' 2 | # References {-} 3 | '` 4 | -------------------------------------------------------------------------------- /BackUp/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/happyrabbit/IntroDataScience/85dd2eaf16ca4e43968e30bdf963b2f5dd26bd32/BackUp/.DS_Store -------------------------------------------------------------------------------- /BackUp/08-FeatureEngineering.Rmd: -------------------------------------------------------------------------------- 1 | # Feature Engineering 2 | 3 | ## Feature Construction 4 | 5 | ## Feature Extraction 6 | 7 | ## Feature Selection 8 | 9 | ### Filter Method 10 | ### Wrapper Method -------------------------------------------------------------------------------- /BackUp/gd_algorithm.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "GradientDescent" 3 | author: "Hui Lin" 4 | date: "5/3/2019" 5 | output: html_document 6 | --- 7 | 8 | ```{r setup, include=FALSE} 9 | knitr::opts_chunk$set(echo = TRUE) 10 | ``` 11 | 12 | Initialize $w_1$, $w_2$, ... , $w_{n_x}$, $b$ 13 | 14 | 15 | Initialize $J=0$, $dw_1=0$, $dw_2=0$, ... , $dw_{n_x} = 0$, $db = 0$ 16 | 17 | For i = 1, ..., m, calculate: 18 | 19 | $z^{(i)} = w^{T}x^{(i)} + b = w_1x_1^{(i)} + w_2x_2^{(i)} + ... + w_{n_x}x_{n_x}^{(i)} + b$ 20 | 21 | $a^{(i)} = \hat{y}^{(i)}= \sigma(z^{(i)}) = \frac{1}{1+e^{-z^{(i)}}}$ 22 | 23 | $J^{(i)} = -y^{(i)}loga^{(i)} - (1-y^{(i)})log(1-a^{(i)})$ 24 | 25 | $\frac{dJ^{(i)}}{da^{(i)}} = \frac{1-y^{(i)}}{1-a^{(i)}} - \frac{y^{(i)}}{a^{(i)}}$ 26 | 27 | $\frac{da^{(i)}}{dz^{(i)}} = a^{(i)}(1-a^{(i)})$ 28 | 29 | $\frac{z^{(i)}}{dw_1} = x_1^{(i)}$ 30 | 31 | $\vdots$ 32 | 33 | $\frac{z^{(i)}}{dw_{n_x}} = x_{n_x}^{(i)}$ 34 | 35 | $\frac{dz^{(i)}}{db} = 1$ 36 | 37 | $\frac{dJ^{(i)}}{dw_1} = \frac{dJ^{(i)}}{da^{(i)}}\frac{da^{(i)}}{dz^{(i)}}\frac{dz^{(i)}}{dw_1} = [a^{(i)}(1-y^{(i)})-(1-a^{(i)})y^{(i)}]x_1^{(i)}$ 38 | 39 | $\vdots$ 40 | 41 | $\frac{dJ^{(i)}}{dw_{n_x}} = [a^{(i)}(1-y^{(i)})-(1-a^{(i)})y^{(i)}]x_{n_x}^{(i)}$ 42 | 43 | $\frac{dJ^{(i)}}{db} = \frac{dJ^{(i)}}{da^{(i)}}\frac{da^{(i)}}{dz^{(i)}}\frac{dz^{(i)}}{db} = a^{(i)}(1-y^{(i)})-(1-a^{(i)})y^{(i)}$ 44 | 45 | $J += J^{(i)}$ 46 | 47 | $dw_1 += \frac{dJ^{(i)}}{dw_1}$ 48 | 49 | $\vdots$ 50 | 51 | $dw_{n_x} += \frac{dJ^{(i)}}{dw_{n_x}}$ 52 | 53 | $db += \frac{dJ^{(i)}}{db}$ 54 | 55 | $J=\frac{J}{m}$, $dw_1=\frac{dw_1}{m}$, ... , $dw_{n_x}=\frac{dw_{n_x}}{m}$, $db =\frac{db}{m}$ 56 | 57 | $w_1 = w_1 - \gamma dw_1$ 58 | 59 | $\vdots$ 60 | 61 | $w_{n_x} = w_{n_x} - \gamma dw_{n_x}$ 62 | 63 | $b = b - \gamma db$ 64 | -------------------------------------------------------------------------------- /CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Code of Conduct 2 | 3 | As contributors and maintainers of this project, we pledge to respect all people who 4 | contribute through reporting issues, posting feature requests, updating documentation, 5 | submitting pull requests or patches, and other activities. 6 | 7 | We are committed to making participation in this project a harassment-free experience for 8 | everyone, regardless of level of experience, gender, gender identity and expression, 9 | sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion. 10 | 11 | Examples of unacceptable behavior by participants include the use of sexual language or 12 | imagery, derogatory comments or personal attacks, trolling, public or private harassment, 13 | insults, or other unprofessional conduct. 14 | 15 | Project maintainers have the right and responsibility to remove, edit, or reject comments, 16 | commits, code, wiki edits, issues, and other contributions that are not aligned to this 17 | Code of Conduct. Project maintainers who do not follow the Code of Conduct may be removed 18 | from the project team. 19 | 20 | Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by 21 | opening an issue or contacting one or more of the project maintainers. 22 | 23 | This Code of Conduct is adapted from the Contributor Covenant 24 | (http:contributor-covenant.org), version 1.0.0, available at 25 | http://contributor-covenant.org/version/1/0/0/ -------------------------------------------------------------------------------- /Data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/happyrabbit/IntroDataScience/85dd2eaf16ca4e43968e30bdf963b2f5dd26bd32/Data/.DS_Store -------------------------------------------------------------------------------- /Data/ResponseError.csv: -------------------------------------------------------------------------------- 1 | "Noise","variable","value" 2 | 0,"rsq_linear",0.599153526293362 3 | 0.5,"rsq_linear",0.533391656950674 4 | 1,"rsq_linear",0.418872082036745 5 | 1.5,"rsq_linear",0.314022648248633 6 | 2,"rsq_linear",0.24845370644907 7 | 2.5,"rsq_linear",0.149401515792994 8 | 3,"rsq_linear",0.101458947593556 9 | 0,"rsq_pls",0.601233144833993 10 | 0.5,"rsq_pls",0.535812452504756 11 | 1,"rsq_pls",0.42188701286924 12 | 1.5,"rsq_pls",0.317581545015178 13 | 2,"rsq_pls",0.252352779307959 14 | 2.5,"rsq_pls",0.15381447809757 15 | 3,"rsq_pls",0.106120639175431 16 | 0,"rsq_mars",0.737211733993056 17 | 0.5,"rsq_mars",0.681828245010142 18 | 1,"rsq_mars",0.53646827877964 19 | 1.5,"rsq_mars",0.411971484896757 20 | 2,"rsq_mars",0.308017834282724 21 | 2.5,"rsq_mars",0.19355853127587 22 | 3,"rsq_mars",0.14040286606564 23 | 0,"rsq_svm",0.717800857870542 24 | 0.5,"rsq_svm",0.624657771501072 25 | 1,"rsq_svm",0.459112559748423 26 | 1.5,"rsq_svm",0.356895590742908 27 | 2,"rsq_svm",0.246187154478104 28 | 2.5,"rsq_svm",0.136849036130088 29 | 3,"rsq_svm",0.111241971599697 30 | 0,"rsq_rf",0.719843163769436 31 | 0.5,"rsq_rf",0.636852420407818 32 | 1,"rsq_rf",0.484237108958198 33 | 1.5,"rsq_rf",0.344300707931917 34 | 2,"rsq_rf",0.244169828849978 35 | 2.5,"rsq_rf",0.0975068987573254 36 | 3,"rsq_rf",0.0299333439729179 37 | -------------------------------------------------------------------------------- /Data/VariableError.csv: -------------------------------------------------------------------------------- 1 | "Noise","variable","value" 2 | 0,"rsq_linear",0.599153526293362 3 | 0.5,"rsq_linear",0.586267584534364 4 | 1,"rsq_linear",0.570801381480329 5 | 1.5,"rsq_linear",0.558314707929088 6 | 2,"rsq_linear",0.554471923439508 7 | 2.5,"rsq_linear",0.557897609642527 8 | 3,"rsq_linear",0.553363166052883 9 | 0,"rsq_pls",0.601233144833993 10 | 0.5,"rsq_pls",0.588414056209932 11 | 1,"rsq_pls",0.573028092860457 12 | 1.5,"rsq_pls",0.560606201013762 13 | 2,"rsq_pls",0.55678335314929 14 | 2.5,"rsq_pls",0.560191266661243 15 | 3,"rsq_pls",0.555680348070766 16 | 0,"rsq_mars",0.737211733993056 17 | 0.5,"rsq_mars",0.724098938054942 18 | 1,"rsq_mars",0.757241035585258 19 | 1.5,"rsq_mars",0.752505102568357 20 | 2,"rsq_mars",0.726293785934675 21 | 2.5,"rsq_mars",0.724098938054942 22 | 3,"rsq_mars",0.726510129590761 23 | 0,"rsq_svm",0.714475231347556 24 | 0.5,"rsq_svm",0.709374734206729 25 | 1,"rsq_svm",0.698957495368727 26 | 1.5,"rsq_svm",0.723143326196015 27 | 2,"rsq_svm",0.707492745435177 28 | 2.5,"rsq_svm",0.693121766775508 29 | 3,"rsq_svm",0.705460443785031 30 | 0,"rsq_rf",0.716517319710388 31 | 0.5,"rsq_rf",0.711318804459765 32 | 1,"rsq_rf",0.716605802277449 33 | 1.5,"rsq_rf",0.732952946798987 34 | 2,"rsq_rf",0.72113997375705 35 | 2.5,"rsq_rf",0.708053870120591 36 | 3,"rsq_rf",0.718788753353135 37 | -------------------------------------------------------------------------------- /Data/avnnetTune.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/happyrabbit/IntroDataScience/85dd2eaf16ca4e43968e30bdf963b2f5dd26bd32/Data/avnnetTune.RData -------------------------------------------------------------------------------- /Data/divisions.csv: -------------------------------------------------------------------------------- 1 | state,division 2 | Alabama,East South Central 3 | Alaska,Pacific 4 | Arizona,Mountain 5 | Arkansas,West South Central 6 | California,Pacific 7 | Colorado,Mountain 8 | Connecticut,New England 9 | Delaware,South Atlantic 10 | Florida,South Atlantic 11 | Georgia,South Atlantic 12 | Hawaii,Pacific 13 | Idaho,Mountain 14 | Illinois,East North Central 15 | Indiana,East North Central 16 | Iowa,West North Central 17 | Kansas,West North Central 18 | Kentucky,East South Central 19 | Louisiana,West South Central 20 | Maine,New England 21 | Maryland,South Atlantic 22 | Massachusetts,New England 23 | Michigan,East North Central 24 | Minnesota,West North Central 25 | Mississippi,East South Central 26 | Missouri,West North Central 27 | Montana,Mountain 28 | Nebraska,West North Central 29 | Nevada,Mountain 30 | New Hampshire,New England 31 | New Jersey,Middle Atlantic 32 | New Mexico,Mountain 33 | New York,Middle Atlantic 34 | North Carolina,South Atlantic 35 | North Dakota,West North Central 36 | Ohio,East North Central 37 | Oklahoma,West South Central 38 | Oregon,Pacific 39 | Pennsylvania,Middle Atlantic 40 | Rhode Island,New England 41 | South Carolina,South Atlantic 42 | South Dakota,West North Central 43 | Tennessee,East South Central 44 | Texas,West South Central 45 | Utah,Mountain 46 | Vermont,New England 47 | Virginia,South Atlantic 48 | Washington,Pacific 49 | West Virginia,South Atlantic 50 | Wisconsin,East North Central 51 | Wyoming,Mountain 52 | -------------------------------------------------------------------------------- /Data/gbmTuneNN.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/happyrabbit/IntroDataScience/85dd2eaf16ca4e43968e30bdf963b2f5dd26bd32/Data/gbmTuneNN.Rdata -------------------------------------------------------------------------------- /Data/nnetTune.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/happyrabbit/IntroDataScience/85dd2eaf16ca4e43968e30bdf963b2f5dd26bd32/Data/nnetTune.RData -------------------------------------------------------------------------------- /Data/segmentdb.csv: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Customer Segmentation Dashboard" 3 | output: 4 | flexdashboard::flex_dashboard: 5 | orientation: rows 6 | vertical_layout: fill 7 | source_code: embed 8 | social: menu 9 | theme: flatly 10 | runtime: shiny 11 | --- 12 | 13 | ```{r setup, include=FALSE} 14 | library(shiny) 15 | library(leaflet) # devtools::install_github('rstudio/leaflet') 16 | library(highcharter) # devtools::install_github('jbkunst/highcharter') 17 | library(plotly) # devtools::install_github('ropensci/plotly') 18 | library(ggplot2) # devtools::install_github('hadley/ggplot2') 19 | library(sp) 20 | library(dplyr) 21 | library(flexdashboard) # devtools::install_github('rstudio/flexdashboard') 22 | library(rgeos) 23 | library(mapproj) 24 | library(maptools) 25 | library(readr) 26 | library(ggthemes) 27 | library(viridis) 28 | library(DataScienceR) 29 | require(htmlwidgets) 30 | require(rgdal) 31 | require(rJava) 32 | library(RColorBrewer) 33 | library(metricsgraphics) 34 | library(DT) 35 | 36 | sim.dat<-readr::read_csv("https://raw.githubusercontent.com/happyrabbit/DataScientistR/master/Data/SegData.csv")%>% 37 | filter(!is.na(income) & age<100) 38 | 39 | ``` 40 | 41 | Sidebar {.sidebar data-width=350} 42 | ====================================================================== 43 | 44 | ```{r} 45 | selectInput('seg', 'Segment', unique(sim.dat$segment)) 46 | selectInput('xcol', 'X Variable', c("age")) 47 | selectInput('ycol', 'Y Variable', c("store_exp","online_exp","store_trans","online_trans")) 48 | ``` 49 | 50 | It is an application using `flexdashboard` and `shiny` in R. 51 | 52 | - __Segment__ : customer segment 53 | 54 | - __X Variable__ : x-axis variable 55 | 56 | - __Y Variable__ : y-axis variable 57 | 58 | Application author: [Hui Lin](http://scientistcafe.com); 59 | Please contact for any questions. 60 | 61 | 62 | 63 | Segment Profile 64 | ====================================================================== 65 | 66 | Row {data-width=650} 67 | ----------------------------------------------------------------------- 68 | ### Conspicuous 69 | 70 | ```{r} 71 | value <- table(sim.dat$segment) 72 | value <- as.vector(round(value/sum(value),2)*100) 73 | 74 | gauge(value[1], min = 0, max = 100, symbol = '%', gaugeSectors( 75 | success = c(30, 100),warning = c(21, 29), danger = c(0, 20) 76 | )) 77 | ``` 78 | 79 | ### Price Sensitive 80 | 81 | ```{r} 82 | gauge(value[2], min = 0, max = 100, symbol = '%', gaugeSectors( 83 | success = c(30, 100),warning = c(21, 29), danger = c(0, 20) 84 | )) 85 | ``` 86 | 87 | ### Quality 88 | 89 | ```{r} 90 | gauge(value[3], min = 0, max = 100, symbol = '%', gaugeSectors( 91 | success = c(30, 100),warning = c(21, 29), danger = c(0, 20) 92 | )) 93 | ``` 94 | 95 | ### Style 96 | 97 | ```{r} 98 | gauge(value[4], min = 0, max = 100, symbol = '%', gaugeSectors( 99 | success = c(30, 100),warning = c(21, 29), danger = c(0, 20) 100 | )) 101 | ``` 102 | 103 | 104 | Row {data-width=650} 105 | ----------------------------------------------------------------------- 106 | 107 | ### Transactional Behavior by Age 108 | 109 | 110 | ```{r} 111 | 112 | selectedData <- reactive({ 113 | dplyr::filter(sim.dat, segment == input$seg) 114 | }) 115 | 116 | renderMetricsgraphics({ 117 | mjs_plot(selectedData(), x= input$xcol, y=input$ycol) %>% 118 | mjs_point(color_accessor=income, size_accessor=income) %>% 119 | mjs_labs(x=input$xcol, y=input$ycol) 120 | }) 121 | 122 | ``` 123 | 124 | 125 | Profile Summary 126 | ====================================================================== 127 | 128 | Row {data-width=350} 129 | ----------------------------------------------------------------------- 130 | 131 | ### Segment Summary Table 132 | 133 | ```{r} 134 | sim.dat%>% 135 | group_by(segment)%>% 136 | summarise(Age=round(mean(na.omit(age)),0), 137 | FemalePct=round(mean(gender=="Female"),2), 138 | HouseYes=round(mean(house=="Yes"),2), 139 | store_exp=round(mean(na.omit(store_exp),trim=0.1),0), 140 | online_exp=round(mean(online_exp),0), 141 | store_trans=round(mean(store_trans),1), 142 | online_trans=round(mean(online_trans),1))%>% 143 | data.frame()%>% 144 | datatable( rownames = FALSE, 145 | caption = 'Table 1: Segment Summary Table', 146 | options = list( 147 | pageLength = 4, 148 | autoWidth = TRUE) 149 | ) 150 | 151 | ``` 152 | 153 | Row {data-width=650} 154 | ----------------------------------------------------------------------- 155 | 156 | ### InsightS 157 | 158 | - Conspicuous: average age is about 40. 1/3 of them are female, and 2/3 are male. They may be a good target for candy dad. They buy regardless the price. Almost all of them own house (0.86). 159 | 160 | - Price: They are older people, average age 60. Nearly all of them own a house(0.94). They are less likely to purchase online (store_trans=6 while online_trans=3). This is the only group that is less likely to purchase online. 161 | 162 | - Quality: The average age is 35. They are not way different with Conspicuous in terms of age. But they spend much less. The percentages of male and female are similar. They prefer online shopping. More than half of them don't own a house (0.66). 163 | 164 | - Style: They are young people with average age 24. The majority of them are female (0.81). Most of them don't own a house (0.73). They are very likely to be digital natives and definitely prefer online shopping. 165 | -------------------------------------------------------------------------------- /Data/segmentdb_server.csv: -------------------------------------------------------------------------------- 1 | Row {data-width=650} 2 | ----------------------------------------------------------------------- 3 | 4 | ### Transactional Behavior by Age 5 | 6 | 7 | ```{r} 8 | 9 | selectedData <- reactive({ 10 | dplyr::filter(sim.dat, segment == input$seg) 11 | }) 12 | 13 | renderMetricsgraphics({ 14 | mjs_plot(selectedData(), x= input$xcol, y=input$ycol) %>% 15 | mjs_point(color_accessor=income, size_accessor=income) %>% 16 | mjs_labs(x=input$xcol, y=input$ycol) 17 | }) 18 | 19 | ``` 20 | 21 | 22 | 23 | -------------------------------------------------------------------------------- /Data/segmentdb_ui.csv: -------------------------------------------------------------------------------- 1 | Sidebar {.sidebar data-width=350} 2 | ====================================================================== 3 | 4 | ```{r} 5 | selectInput('seg', 'Segment', unique(sim.dat$segment)) 6 | selectInput('xcol', 'X Variable', c("age")) 7 | selectInput('ycol', 'Y Variable', c("store_exp","online_exp","store_trans","online_trans")) 8 | ``` 9 | -------------------------------------------------------------------------------- /Data/tree_sample.csv: -------------------------------------------------------------------------------- 1 | "Birthday","CS" 2 | "06-16","No" 3 | "11-22","Yes" 4 | "04-22","No" 5 | "05-01","Yes" 6 | "11-06","No" 7 | "08-11","No" 8 | "05-01","Yes" 9 | "05-27","Yes" 10 | "08-06","Yes" 11 | "08-24","Yes" 12 | "05-20","Yes" 13 | "10-19","No" 14 | "06-20","No" 15 | "03-11","No" 16 | "12-06","No" 17 | "12-04","No" 18 | "03-18","No" 19 | "12-01","Yes" 20 | "06-28","Yes" 21 | "09-08","No" 22 | "09-23","No" 23 | "06-08","Yes" 24 | "01-01","No" 25 | "07-20","Yes" 26 | "06-21","Yes" 27 | "12-03","No" 28 | "11-13","Yes" 29 | "12-25","No" 30 | "10-29","No" 31 | "05-28","Yes" 32 | "07-29","Yes" 33 | "08-11","No" 34 | "08-02","No" 35 | "12-29","Yes" 36 | "01-08","No" 37 | "04-25","Yes" 38 | "09-26","Yes" 39 | "04-03","Yes" 40 | "12-21","Yes" 41 | "11-02","No" 42 | "09-03","Yes" 43 | "05-24","No" 44 | "10-01","No" 45 | "06-19","Yes" 46 | "01-24","No" 47 | "10-17","Yes" 48 | "05-07","No" 49 | "10-03","Yes" 50 | "02-25","Yes" 51 | "05-31","No" 52 | -------------------------------------------------------------------------------- /IDS.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: knitr 13 | LaTeX: pdfLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | 18 | BuildType: Website 19 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | CC0 1.0 Universal 2 | 3 | Statement of Purpose 4 | 5 | The laws of most jurisdictions throughout the world automatically confer 6 | exclusive Copyright and Related Rights (defined below) upon the creator and 7 | subsequent owner(s) (each and all, an "owner") of an original work of 8 | authorship and/or a database (each, a "Work"). 9 | 10 | Certain owners wish to permanently relinquish those rights to a Work for the 11 | purpose of contributing to a commons of creative, cultural and scientific 12 | works ("Commons") that the public can reliably and without fear of later 13 | claims of infringement build upon, modify, incorporate in other works, reuse 14 | and redistribute as freely as possible in any form whatsoever and for any 15 | purposes, including without limitation commercial purposes. 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Affirmer understands and acknowledges that Creative Commons is not a 112 | party to this document and has no duty or obligation with respect to this 113 | CC0 or use of the Work. 114 | 115 | For more information, please see 116 | 117 | 118 | -------------------------------------------------------------------------------- /Python/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/happyrabbit/IntroDataScience/85dd2eaf16ca4e43968e30bdf963b2f5dd26bd32/Python/.DS_Store -------------------------------------------------------------------------------- /Python/TokenizingPadding.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "TokenizingPadding.ipynb", 7 | "provenance": [], 8 | "authorship_tag": "ABX9TyMxGgewAXnd85ykZT0EEQ7C", 9 | "include_colab_link": true 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | } 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "markdown", 19 | "metadata": { 20 | "id": "view-in-github", 21 | "colab_type": "text" 22 | }, 23 | "source": [ 24 | "\"Open" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "metadata": { 30 | "id": "XqUWz7TYKsLp", 31 | "colab_type": "code", 32 | "colab": {} 33 | }, 34 | "source": [ 35 | "# Load packages\n", 36 | "import tensorflow as tf\n", 37 | "from tensorflow import keras\n", 38 | "from tensorflow.keras.preprocessing.text import Tokenizer\n", 39 | "from tensorflow.keras.preprocessing.sequence import pad_sequences" 40 | ], 41 | "execution_count": 0, 42 | "outputs": [] 43 | }, 44 | { 45 | "cell_type": "markdown", 46 | "metadata": { 47 | "id": "ISZqTXTAG2kE", 48 | "colab_type": "text" 49 | }, 50 | "source": [ 51 | "We give each word a value and use those values to train a neural network. Consider the following review sentences:" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "metadata": { 57 | "id": "__pxtJR3oUMD", 58 | "colab_type": "code", 59 | "colab": {} 60 | }, 61 | "source": [ 62 | "sentences = ['This movie is great!',\n", 63 | "'Great movie ? Are you kidding me ! Not worth the money.',\n", 64 | "'Love it']" 65 | ], 66 | "execution_count": 0, 67 | "outputs": [] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": { 72 | "id": "5qcf9agvSGRA", 73 | "colab_type": "text" 74 | }, 75 | "source": [ 76 | "We set `num_words = 100` which is the number of distinct words. It is way too big for this baby example. If you're creating a training set based on lots of text, you usually don't know how many distinct words there are in the text. So by setting this hyperparameter, the tokenizer will take the top 100 words by volume and just encode those. It's a handy shortcut when dealing with lots of data, and worth experimenting with when you train with real data. Sometimes the impact of less words can be minimal on training accuracy, but huge on training time.\n", 77 | "\n", 78 | "`oov_token = \"\"` specifies the token for outer vocabulary to be used for words that aren't in the word index. " 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "metadata": { 84 | "id": "vZoeg5Z8K9D1", 85 | "colab_type": "code", 86 | "colab": {} 87 | }, 88 | "source": [ 89 | "tokenizer = Tokenizer(num_words= 100, oov_token= \"\")" 90 | ], 91 | "execution_count": 0, 92 | "outputs": [] 93 | }, 94 | { 95 | "cell_type": "markdown", 96 | "metadata": { 97 | "id": "FmiZ5n9GS0xR", 98 | "colab_type": "text" 99 | }, 100 | "source": [ 101 | "`fit_on_texts` method of the `tokenizer` encodes the sentences. The tokenizer has a `word_index` property which returns a dictionary containing key value pairs, where the key is the word, and the value is the token for that word. You can inspect by simply printing it out. Note that the tokenizer lower-cases all words and strips punctuation out. " 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "metadata": { 107 | "id": "D2LXQjHbS3Wc", 108 | "colab_type": "code", 109 | "colab": {} 110 | }, 111 | "source": [ 112 | "tokenizer.fit_on_texts(sentences)\n", 113 | "word_index = tokenizer.word_index\n", 114 | "\n", 115 | "# call the tokenizer to get texts to sequences\n", 116 | "sequences = tokenizer.texts_to_sequences(sentences)\n", 117 | "padded = pad_sequences(sequences, maxlen=6, padding = \"post\", truncating = 'post')" 118 | ], 119 | "execution_count": 0, 120 | "outputs": [] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": { 125 | "id": "baDi5t2AQ2ff", 126 | "colab_type": "text" 127 | }, 128 | "source": [ 129 | "![](https://course2020.scientistcafe.com/slides/02DeepLearning/RNN/images/TokenizingPadding.png)" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "metadata": { 135 | "id": "auCW1Qnpovl2", 136 | "colab_type": "code", 137 | "colab": { 138 | "base_uri": "https://localhost:8080/", 139 | "height": 190 140 | }, 141 | "outputId": "421aae75-6966-47c5-9f71-17f078285b68" 142 | }, 143 | "source": [ 144 | "print(\"\\nWord Index = \", word_index)\n", 145 | "print(\"\\nSequences = \", sequences)\n", 146 | "print(\"\\nPadded Sequences:\")\n", 147 | "print(padded)" 148 | ], 149 | "execution_count": 41, 150 | "outputs": [ 151 | { 152 | "output_type": "stream", 153 | "text": [ 154 | "\n", 155 | "Word Index = {'': 1, 'movie': 2, 'great': 3, 'this': 4, 'is': 5, 'are': 6, 'you': 7, 'kidding': 8, 'me': 9, 'not': 10, 'worth': 11, 'the': 12, 'money': 13, 'love': 14, 'it': 15}\n", 156 | "\n", 157 | "Sequences = [[4, 2, 5, 3], [3, 2, 6, 7, 8, 9, 10, 11, 12, 13], [14, 15]]\n", 158 | "\n", 159 | "Padded Sequences:\n", 160 | "[[ 4 2 5 3 0 0]\n", 161 | " [ 3 2 6 7 8 9]\n", 162 | " [14 15 0 0 0 0]]\n" 163 | ], 164 | "name": "stdout" 165 | } 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "metadata": { 171 | "id": "H4iqQTZGo1TF", 172 | "colab_type": "code", 173 | "colab": {} 174 | }, 175 | "source": [ 176 | "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])" 177 | ], 178 | "execution_count": 0, 179 | "outputs": [] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "metadata": { 184 | "id": "Qyu68V5vpc9-", 185 | "colab_type": "code", 186 | "colab": { 187 | "base_uri": "https://localhost:8080/", 188 | "height": 34 189 | }, 190 | "outputId": "863a9173-4709-4bb6-e9d4-a62718a31d55" 191 | }, 192 | "source": [ 193 | "def decode_review(text):\n", 194 | " return ' '.join([reverse_word_index.get(i, '?') for i in text])\n", 195 | "print(decode_review(padded[1]))" 196 | ], 197 | "execution_count": 36, 198 | "outputs": [ 199 | { 200 | "output_type": "stream", 201 | "text": [ 202 | "great movie are you kidding\n" 203 | ], 204 | "name": "stdout" 205 | } 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "metadata": { 211 | "id": "nKtg9Iw3phRI", 212 | "colab_type": "code", 213 | "colab": {} 214 | }, 215 | "source": [ 216 | "" 217 | ], 218 | "execution_count": 0, 219 | "outputs": [] 220 | } 221 | ] 222 | } -------------------------------------------------------------------------------- /R/GenerateTreePlots.R: -------------------------------------------------------------------------------- 1 | path <- 'https://raw.githubusercontent.com/guru99-edu/R-Programming/master/titanic_data.csv' 2 | titanic <-read.csv(path) 3 | titanic$age <- as.numeric(titanic$age) 4 | titanic$fare <- as.numeric(titanic$fare) 5 | 6 | idx <- titanic$survived == 1 7 | titanic$survived[idx] <- "survived" 8 | titanic$survived[!idx] <- "died" 9 | titanic$survived <- as.factor(titanic$survived) 10 | 11 | tree_fit <- rpart(survived ~ sex + age, data=titanic) 12 | plot(as.party(tree_fit)) 13 | 14 | prp(tree_fit, extra = 6, prefix = "fraction\n") 15 | 16 | decisionplot(tree_fit, x, class = "survived", main = "") 17 | 18 | 19 | ti_tree <- decision_tree() %>% 20 | set_engine("rpart") %>% 21 | set_mode("classification") %>% 22 | fit(survived ~ sex + age, data = titanic) 23 | 24 | titanic %>% 25 | ggplot(aes(x=sex, y=age)) + 26 | geom_jitter(aes(col=survived), alpha=0.7) + 27 | geom_parttree(data = ti_tree, aes(fill=survived), alpha = 0.1) + 28 | theme_minimal() 29 | 30 | x <- iris[, c("Petal.Length", "Petal.Width", "Species")] 31 | model <- rpart(Species ~ ., data=x) 32 | decisionplot(model, x, class = "Species", main = "", 33 | xlab = 'Petal Length', ylab = "Petal Width") 34 | -------------------------------------------------------------------------------- /R/TreeBasedModel.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "TreeModel" 3 | output: 4 | md_document: 5 | variant: markdown_github 6 | --- 7 | 8 | ```{r setup, include=FALSE} 9 | knitr::opts_chunk$set(echo = TRUE) 10 | ``` 11 | 12 | ## R Markdown 13 | 14 | This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see . 15 | 16 | When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this: 17 | 18 | ```{r cars} 19 | summary(cars) 20 | ``` 21 | 22 | ## Including Plots 23 | 24 | You can also embed plots, for example: 25 | 26 | ```{r pressure, echo=FALSE} 27 | plot(pressure) 28 | ``` 29 | 30 | Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot. 31 | -------------------------------------------------------------------------------- /_bookdown.yml: -------------------------------------------------------------------------------- 1 | book_filename: "IDS" 2 | chapter_name: "Chapter " 3 | output_dir: "/Users/hui/Documents/GitHub/linhui.org/IDS" 4 | clean: [packages.bib] 5 | delete_merged_file: true 6 | language: 7 | label: 8 | fig: "FIGURE " 9 | tab: "TABLE " 10 | ui: 11 | edit: "Edit" 12 | chapter_name: "Chapter " 13 | 14 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/happyrabbit/IntroDataScience/85dd2eaf16ca4e43968e30bdf963b2f5dd26bd32/_bookdown_files/IDS_files/.DS_Store -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/activationleakyrelu-1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | -5 39 | 0 40 | 5 41 | 42 | 43 | 44 | 45 | 46 | 47 | 0 48 | 2 49 | 4 50 | 6 51 | 8 52 | 53 | z 54 | 55 | 56 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/activationrelu-1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | -5 39 | 0 40 | 5 41 | 42 | 43 | 44 | 45 | 46 | 47 | 0 48 | 2 49 | 4 50 | 6 51 | 8 52 | 53 | z 54 | 55 | 56 | R(z) = max(0, z) 57 | 58 | 59 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/activationsigmoid-1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | -5 39 | 0 40 | 5 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 0.0 49 | 0.2 50 | 0.4 51 | 0.6 52 | 0.8 53 | 1.0 54 | 55 | z 56 | σ 57 | (z) 58 | 59 | 60 | σ 61 | (z) = 62 | 1 63 | 1 64 | + 65 | e 66 | − 67 | z 68 | 69 | 70 | 71 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/activationtanh-1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | -5 39 | 0 40 | 5 41 | 42 | 43 | 44 | 45 | 46 | 47 | -1.0 48 | -0.5 49 | 0.0 50 | 0.5 51 | 1.0 52 | 53 | z 54 | 55 | 56 | tanh(z) = 57 | e 58 | z 59 | − 60 | e 61 | − 62 | z 63 | e 64 | z 65 | + 66 | e 67 | − 68 | z 69 | 70 | 71 | 72 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/lassoregtune-1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | Fraction 26 | RMSE (Cross-Validation) 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 1745 46 | 1750 47 | 1755 48 | 1760 49 | 50 | 51 | 52 | 53 | 54 | 0.80 55 | 0.85 56 | 0.90 57 | 0.95 58 | 1.00 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/ridgeregtune-1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | Weight Decay 26 | RMSE (Cross-Validation) 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 1750 46 | 1760 47 | 1770 48 | 49 | 50 | 51 | 52 | 53 | 54 | 0.00 55 | 0.02 56 | 0.04 57 | 0.06 58 | 0.08 59 | 0.10 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/times-1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | AR 35 | ( 36 | 1 37 | ) 38 | 39 | 40 | 41 | φ 42 | = 43 | − 44 | 0.9 45 | Time 46 | timedata 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 0 55 | 20 56 | 40 57 | 60 58 | 80 59 | 100 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | -6 69 | -4 70 | -2 71 | 0 72 | 2 73 | 4 74 | 6 75 | 76 | 77 | 78 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/unnamed-chunk-137-1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | Max Tree Depth 26 | RMSE (Cross-Validation) 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 1650 47 | 1700 48 | 1750 49 | 1800 50 | 51 | 52 | 53 | 54 | 55 | 56 | 2 57 | 4 58 | 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41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 0 75 | 2 76 | 4 77 | 6 78 | 8 79 | 10 80 | 12 81 | 82 | rfit 83 | MeanDecreaseGini 84 | 85 | 86 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/unnamed-chunk-70-1.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 0.0 34 | 0.2 35 | 0.4 36 | 0.6 37 | 0.8 38 | 1.0 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 0.0 47 | 0.2 48 | 0.4 49 | 0.6 50 | 0.8 51 | 1.0 52 | 53 | 1 - Specificities 54 | Sensitivities 55 | 56 | -------------------------------------------------------------------------------- /_bookdown_files/IDS_files/figure-html/unnamed-chunk-72-1.svg: 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-------------------------------------------------------------------------------- 1 | \usepackage{booktabs} 2 | \usepackage{longtable} 3 | \usepackage{listings} 4 | \usepackage[bf,singlelinecheck=off]{caption} 5 | \usepackage{hyperref} 6 | \usepackage{framed,color} 7 | \definecolor{shadecolor}{RGB}{248,248,248} 8 | 9 | \renewcommand{\textfraction}{0.05} 10 | \renewcommand{\topfraction}{0.8} 11 | \renewcommand{\bottomfraction}{0.8} 12 | \renewcommand{\floatpagefraction}{0.75} 13 | 14 | \renewenvironment{quote}{\begin{VF}}{\end{VF}} 15 | \let\oldhref\href 16 | \renewcommand{\href}[2]{#2\footnote{\url{#1}}} 17 | 18 | \makeatletter 19 | \newenvironment{kframe}{% 20 | \medskip{} 21 | \setlength{\fboxsep}{.8em} 22 | \def\at@end@of@kframe{}% 23 | \ifinner\ifhmode% 24 | \def\at@end@of@kframe{\end{minipage}}% 25 | \begin{minipage}{\columnwidth}% 26 | \fi\fi% 27 | \def\FrameCommand##1{\hskip\@totalleftmargin \hskip-\fboxsep 28 | \colorbox{shadecolor}{##1}\hskip-\fboxsep 29 | % There is no \\@totalrightmargin, so: 30 | \hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}% 31 | \MakeFramed {\advance\hsize-\width 32 | \@totalleftmargin\z@ \linewidth\hsize 33 | \@setminipage}}% 34 | {\par\unskip\endMakeFramed% 35 | \at@end@of@kframe} 36 | \makeatother 37 | 38 | \renewenvironment{Shaded}{\begin{kframe}}{\end{kframe}} 39 | 40 | \usepackage{makeidx} 41 | \makeindex 42 | 43 | \urlstyle{tt} 44 | 45 | \lstset{ 46 | breaklines=true 47 | } 48 | 49 | \usepackage{amsmath, bm} 50 | \usepackage{amsthm} 51 | 52 | \makeatletter 53 | \def\thm@space@setup{% 54 | \thm@preskip=8pt plus 2pt minus 4pt 55 | \thm@postskip=\thm@preskip 56 | } 57 | \makeatother 58 | 59 | \frontmatter 60 | --------------------------------------------------------------------------------