Hub
Pricing About
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Community Hub
  • Search

361 results

Filter
Filter by tag
Analytics
Classification Machine learning KNIME SAP XGBoost Advanced analytics Cross validation Data mining Integrations Weka
+7
  1. Go to item
    Node / LoopEnd
    Parameter Optimization Loop End
    Analytics Mining Optimization
    This loop closes a parameter optimization loop. It collects the objective function value from a flow variable and transfer the in…
    0
    knime
  2. Go to item
    Node / LoopStart
    Parameter Optimization Loop Start
    Analytics Mining Optimization
    This loop starts a parameter optimization loop. In the dialog you can enter several parameters with an interval and a step size. …
    1
    knime
  3. Go to item
    Node / LoopStart
    Parameter Optimization Loop Start (Table)
    Analytics Mining Optimization
    This loop starts a parameter optimization loop. It takes a table as input. In the dialog you can map the parameters of the table …
    0
    knime
  4. Go to item
    Workflow
    Logistic Regression (verificare se si è in overfitting)
    Classification Machine learning Prediction
    +6
    Per verificare se i valori elevati di accuratezza dell'algoritmo Logistic regression non siano dovuti ad overfitting, in questa v…
    falaimo > Public > KNIME Analytics Platform per Data Scientists, corso base > 06_Logistic_Regression (check overfitting)
    0
    falaimo
  5. Go to item
    Workflow
    Forest Cover Type Classification with XGBoost
    XGBoost Classification Gradient boosting
    +7
    This workflows shows how the XGBoost nodes can be used for classification tasks.
    knime > Examples > 04_Analytics > 16_XGBoost > 01_Classify_Forest_Covertypes_with_XGBoost
    0
    knime
  6. Go to item
    Node / Learner
    KernelLogisticRegression (3.7)
    Analytics Integrations Weka
    +3
    This classifier generates a two-class kernel logistic regression model The model is fit by minimizing the negative log-likelihood…
    0
    knime
  7. Go to item
    Node / Manipulator
    Multiobjective Score Computation
    Analytics Mining Optimization
    +1
    This node takes two input tables: the first table contains all objects/rows from which subsets are selected. The second input tab…
    0
    knime
  8. Go to item
    Node / Other
    Multiobjective Subset Selection (NSGA-II)
    Analytics Mining Optimization
    +1
    This node finds (near)optimal fixed-sized subsets of rows based one one or more criteria. It uses the NSGA-II algorithm to find a…
    0
    knime
  9. Go to item
    Node / Other
    Score Erosion
    Analytics Mining Optimization
    +1
    This node uses the Score Erosion algorithm in order to select subsets of items/rows that have a high overall score, and are as di…
    0
    knime
  10. Go to item
    Node / Learner
    SMO (3.6)
    Analytics Integrations Weka
    +3
    Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation g…
    0
    knime
  11. Go to item
    Node / Learner
    SMO (3.7)
    Analytics Integrations Weka
    +3
    Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation g…
    0
    knime
  12. Go to item
    Node / Learner
    Logistic (3.6)
    Analytics Integrations Weka
    +3
    Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, howeve…
    0
    knime
  13. Go to item
    Node / Learner
    Logistic (3.7)
    Analytics Integrations Weka
    +3
    Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, howeve…
    0
    knime
  14. Go to item
    Node / Learner
    SVM Learner
    Analytics Mining SVM
    This node trains a support vector machine on the input data. It supports a number of different kernels (HyperTangent, Polynomial …
    0
    knime
  15. Go to item
    Node / Manipulator
    Rowset Filter
    Analytics Mining Optimization
    The rows from the input table are filtered according to one selected solution (which is a set of row keys) from a previous optimi…
    0
    knime
  16. Go to item
    Node / Learner
    PART (3.7)
    Analytics Integrations Weka
    +3
    Class for generating a PART decision list Uses separate-and-conquer.Builds a partial C4.5 decision tree in each iteration and mak…
    0
    knime
  17. Go to item
    Node / Learner
    PART (3.6)
    Analytics Integrations Weka
    +3
    Class for generating a PART decision list. Uses separate-and-conquer. Builds a partial C4.5 decision tree in each iteration and m…
    0
    knime
  18. Go to item
    Node / Learner
    SMOreg (3.6)
    Analytics Integrations Weka
    +3
    SMOreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorith…
    0
    knime
  19. Go to item
    Node / Learner
    Logistic Regression Learner
    Analytics Mining Logistic Regression
    Performs a multinomial logistic regression. Select in the dialog a target column (combo box on top), i.e. the response. The solve…
    0
    knime
  20. Go to item
    Node / Learner
    JRip (3.6)
    Analytics Integrations Weka
    +3
    This class implements a propositional rule learner*, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was …
    0
    knime

KNIME
Open for Innovation

KNIME AG
Talacker 50
8001 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • E-Learning course
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • KNIME Open Source Story
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more on KNIME Business Hub
© 2023 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits