René's URL Explorer Experiment

{"@context":"https://schema.org","@type":"DiscussionForumPosting","headline":"Overarching Plan (to MVP / version 1)","articleBody":"Hey Folks!\r\nI thought it might be a bit easier if we had one issue that had the current \"state of the world\". \r\nIt would have a list of all completed Estimators/Functions and next to each it would have a person's name if someone was working on it or it'd be checked if it was complete and merged in dev. \r\n\r\nPing me in the comments beneath and I'll add you to whichever estimators you want to work on.\r\n\r\nI went through the scikit-learn docs yesterday and broke out the Estimators that we would need for an MVP of scikit.js (let's call it version 1). \r\n\r\n### Version 1\r\nThe focus here is on simple models, and all the preprocessing, and metrics that you'd need to perform high quality model generation.\r\n\r\n**linear_model**\r\n- [x] LinearRegression\r\n- [x] LassoRegression\r\n- [x] RidgeRegression\r\n- [x] ElasticNet\r\n- [x] LogisticRegression\r\n- [x] SGDClassifier\r\n- [x] SGDRegressor\r\n\r\n**cluster**\r\n- [x] KMeans \r\n\r\n**neighbors**\r\n- [x] #37\r\n- [x] #38\r\n\r\n**dummy**\r\n- [x] DummyClassifier \r\n- [x] DummyRegressor \r\n\r\n**impute**\r\n- [x] SimpleImputer\r\n\r\n**preprocessing** \r\n- [x] StandardScaler\r\n- [x] MinMaxScaler \r\n- [x] MaxAbsScaler \r\n- [x] Normalizer \r\n- [x] RobustScaler \r\n- [x] LabelEncoder \r\n- [x] OneHotEncoder \r\n- [x] OrdinalEncoder \r\n\r\n**pipeline**\r\n- [x] Pipeline \r\n\r\n**compose**\r\n- [x] ColumnTransformer \r\n\r\n**tree**\r\n- [x] #39\r\n- [x] #40\r\n\r\n**metrics** \r\n- [x] accuracyScore\r\n- [x] confusionMatrix\r\n- [x] hingeLoss\r\n- [x] logLoss\r\n- [x] precisionScore\r\n- [x] recallScore\r\n- [x] rocAucScore  \r\n- [x] zeroOneLoss\r\n- [x] meanAbsoluteError\r\n- [x] meanSquaredError\r\n- [x] meanSquaredLogError\r\n- [x] r2Score\r\n\r\nSo pick whichever ya want, and ping me, and I'll update the issue and put your name next to the Estimator / Function.\r\n\r\n### Some great resources for contributors\r\n* ML from scratch in Python : https://github.com/eriklindernoren/ML-From-Scratch\r\n* Nick Leclure's Book: https://github.com/nfmcclure/tensorflow_cookbook\r\n* Charlie Gerard's Book: Practical Machine Learning with Tensorflow.js\r\n* MachineLearnjs : https://github.com/machinelearnjs/machinelearnjs\r\n\r\nHello folks! Time flies when you're having fun :) \r\nWe are rounding the corner the completion of the MVP / Version 1 list above. I thought it would be good to go through scikit-learn and make a list of the next most important things. That list is below as well as some general todos (docs, tutorials). Feel free to ping me or comment below and grab whatever interests in the following list. \r\n\r\nOnward and Upward! \r\n\r\nlinear_model\r\n- [ ] Exact solution for linear_regression\r\n\r\ndatasets\r\n- [x] Iris\r\n- [x] Boston Housing\r\n- [x] #44\r\n- [x] #50\r\n\r\nnaive_bayes\r\n- [x] #41\r\n- [x] #42\r\n\r\nsvm\r\n- [x] LinearSVC\r\n- [x] LinearSVR\r\n- [x] SVC\r\n- [x] SVR\r\n\r\nmodel_selection\r\n- [ ] GroupKFold\r\n- [x] #46\r\n- [ ] ShuffleSplit\r\n- [x] #45\r\n\r\ndecomposition\r\n- [ ] PCA\r\n\r\nhyper_parameter\r\n- [x] #187\r\n\r\nensemble \r\n- [x] VotingRegressor\r\n- [x] VotingClassifier\r\n- [ ] RandomForestClassifier\r\n- [ ] RandomForestRegressor\r\n\r\ndocs\r\n- [x] Make Basic Docs site\r\n- [x] Push the Basic Docs site to scikit.org. Have scikit.js redirect to scikit.org\r\n- [x] Make Basic Docs site show api for all functions / classes that we export\r\n- [x] Make it build browser and node versions\r\n- [x] Make the tests run against browser and node environments\r\n","author":{"url":"https://github.com/dcrescim","@type":"Person","name":"dcrescim"},"datePublished":"2021-10-11T17:02:51.000Z","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/CommentAction","userInteractionCount":4},"url":"https://github.com/11/scikit.js/issues/11"}