[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-573208-105":53,"doc-detail-573208-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","machine-learning-with-knime-analytics-platform-cheat-sheet","Machine learning with KNIME Analytics Platform - Cheat sheet","","Cheat sheet summarizing core machine learning concepts using the KNIME Analytics Platform. It covers supervised learning for both numeric prediction and classification, including decision trees (C4.5), logistic regression, naive Bayes, support vector machines, and k-nearest neighbors. It also summarizes time series approaches such as numeric prediction via past vectors and SARIMA with parameters for seasonality and differencing. Evaluation topics include numeric error measures (RMSE, MAE, R2) and cross-validation. Ensemble learning methods cover bagging, boosting, random forests, tree ensembles, and prediction fusion.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/machine-learning-with-knime-analytics-platform-cheat-sheet/573208/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/machine-learning-with-knime-analytics-platform-cheat-sheet/573208.png","ImageObject",442,249,{"name":88,"@type":89},"Himbo","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-19","2026-10-13",true,{"@type":98,"interactionType":99,"userInteractionCount":76},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is supervised learning in this cheat sheet?","Question",{"text":108,"@type":109},"Supervised learning maps input features to a target variable using labeled training data, then evaluates generalization on an independent test set with scoring metrics.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"Which models are listed for classification?",{"text":113,"@type":109},"The cheat sheet lists decision trees (C4.5), logistic regression, naive Bayes, support vector machines (SVM), and k-nearest neighbor (kNN).",{"name":115,"@type":106,"acceptedAnswer":116},"How does cross-validation help with model evaluation?",{"text":117,"@type":109},"Cross-validation trains and validates the model N times on different training/test splits, and analyzes the resulting error or accuracy statistics to understand overfitting and generalization.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},573208,1792245601,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":76,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":73,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":9},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","Cheat sheet: Machine learning with KNIME Analytics Platform  \nSupervised learning  \n|  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  |  |  |  |  | \u003Cbr>Numeric prediction\u003Cbr>\u003Cbr>Time Series Analysis\u003Cbr>\u003Cbr>Seasonal Auto-Regressive Integrated Moving Average (SARIMA): A linear Seasonal (S) Auto-Regressive (AR) model is constructed on a specified number p of past (seasonal) values; data are prepared by a degree of differencing d to correct non-stationarity; while a linear combinationnamed Moving Average (MA) - models the q past (seasonal) residual errors. All SARIMA model parameters are estimated concurrently by various algorithms, mostly following the Box–Jenkins approach.\u003Cbr>ML-based TSA: A numeric prediction model trained on vectors of past values can predict the current numeric value of the time series. |  |  |  |  |\n|  | Classification |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |\n| Decision Tree: Follows the C4 .5 decision tree algorithm. These algorithms generate a tree-like structure, creating data subsets, aka tree nodes. At each node, the data are split based on one of the input features, generating two or more branches as output. Further splits are made in subsequent nodes until a node is generated where all or almost all of the data belong to the same class.\u003Cbr>Logistic Regression: A statistical algorithm that models the relationship between the input features and the categorical output classes by maximizing a likelihood function. Originally developed for binary problems, it has been extended to problems with more than two classes (multinomial logistic regression) . |  | Naive Bayes: Based on Bayes'theorem and assuming statistical independence between input features (thus \"naive\"), this algorithm estimates the conditional probability of each output class given the vector of input features. The class with the highest conditional probability is assigned to the input data.\u003Cbr>Support Vector Machine (SVM):\u003Cbr>A supervised algorithm constructing a set of discriminative hyperplanes in high-dimensional space. In addition to linear classification, SVMs can perform non-linear classification by implicitly mapping their inputs into high-dimensional feature spaces, where the two classes are linearly separable.\u003Cbr>k-Nearest Neighbor (kNN):\u003Cbr>A non-parametric method that assigns the class of the k most similar points in the training data, based on a pre-defined distance measure. Class attribution can be weighted by the distance to the k-th point and/or by the class probability. |  |  |  |  |  |  |  |\n|  |  |  |  |  |  |  |  |  |  |\n\nCustom ensemble model:  \nCombining different supervised models to form a custom ensemble model. The final prediction can be based on majority vote as well as on the average or other functions of the output results.  \nXGBoost: An optimized distributed library for machine learning models in the gradient boosting framework, designed to be highly efficient, flexible, and portable. It features regularization parameters to penalize complex models, effective handling of sparse data for better performance, parallel computation, and more efficient memory usage.  \nNumeric error measures: Evaluation metrics for numeric prediction models quantifying the error size and direction. Common metrics include RMSE, MAE, or R2. Most of these metrics depend on the range of the target variable.  \nCross-validation: A model validation technique for assessing how the results of a machine learning model will generalize to an independent dataset. A model is trained and validated N times on different pairs of training set and test set, both extracted from the original dataset. Some basic statistics on the resulting N error or accuracy measures gives insightson overfitting and generalization.  \nResources  \nSupervised Learning: A set of machine learning algorithms to predict the value of a target class or variable. They produce a mapping funct","cbCailRs9PVl5OQ7","https://ap.wps.com/l/cbCailRs9PVl5OQ7","pdf",9735983,"English","# Supervised learning\n## Numeric prediction and time series analysis\n## Classification algorithms\n## Ensemble learning and evaluation","[{\"question\":\"What is supervised learning in this cheat sheet?\",\"answer\":\"Supervised learning maps input features to a target variable using labeled training data, then evaluates generalization on an independent test set with scoring metrics.\"},{\"question\":\"Which models are listed for classification?\",\"answer\":\"The cheat sheet lists decision trees (C4.5), logistic regression, naive Bayes, support vector machines (SVM), and k-nearest neighbor (kNN).\"},{\"question\":\"How does cross-validation help with model evaluation?\",\"answer\":\"Cross-validation trains and validates the model N times on different training/test splits, and analyzes the resulting error or accuracy statistics to understand overfitting and generalization.\"}]","Machine learning with KNIME Analytics Platform - Cheat sheet | PDF",1791934618]