Learn JMP

Predictive Modeling and Machine Learning

• "Tune and Zoom": A Method for Tuning Machine Learning Models that You and Your Students Can Use

  REGISTER HERE   Special Guest Presenter:
  Prof. Nathan Coates
  Faculty Director, Master of Science in Business Analytics
  Villanova University   Make better predictions! The predictive power of machine learning models depends on hyper-parameter selection. Years of university machine learning exercises have led us to develop a method for adjusting hyper-parameters that we call “Tune and Zoom”. This metho...

  Ross_Metusalem

  Learn JMP Events |

  Wednesday, November 19, 2025
  Nov 19

• Identifizierung der Einflüsse von funktionalen Faktoren auf die Zielgrößen

  German Mastering JMP Webinar   Erfahren Sie, wie Sie sequenzielle Messdaten analysieren, bei denen die als Faktoren zu analysierenden Messungen keine einzelnen Punkte, sondern eine Reihe von Punkten sind, die als Kurven dargestellt werden. Beispiele hierfür sind chemische Spektren, Sensoren und Batch- oder Streaming-Daten. Erfahren Sie, wie Sie den gesamten Datenbereich anstelle ausgewählter Punkt...

  maria_astals

  Learn JMP Events |

  Friday, November 21, 2025
  Nov 21

• Identifier l'impact de la courbe d'un facteur sur les réponses

  French Mastering JMP   Découvrez comment analyser des données de mesures séquentielles lorsque ces mesures prises en tant que facteurs ne sont pas des points uniques, mais une gamme de points présentés sous forme de courbes. Il peut s'agir, par exemple, de spectres chimiques, de capteurs, de données par lots ou en flux. Apprenez à utiliser efficacement l'ensemble des données plutôt que des points ...

  maria_astals

  Learn JMP Events |

  Friday, November 21, 2025
  Nov 21

• Identifying the Impact That Curved Factors Have on Responses

  EMEA Mastering JMP   Learn how to analyze sequential measurement data where measurements you want to analyze as factors are not single points, but a range of points presented as curves. Examples include chemical spectra, sensors, batch or streaming data. Learn how to efficiently use the full range of data rather than selected points in the curve to provide immediately interpretable results, includ...

  maria_astals

  Learn JMP Events |

  Friday, November 21, 2025
  Nov 21

• Which Model When

  (view in My Videos)    

  DonMcCormack

  Learning Center |

  Aug 26, 2025 10:00 PM

  304 views | 0 replies

• Model Comparison and Selection

   Use this platform to summarize and compare the performance of multiple statistical models that have been fit to data. For details on fitting different statistical models, see the appropriate guides. Model Comparison – Continuous Response Example:  We use the Body Fat.jmp data to predict Percent body fat. Formulas for several models, saved to the data table, are grouped under Prediction Formulas ...

  gail_massari

  Learning Library |

  Aug 20, 2025 8:42 PM

  73 views | 0 replies

• Creating a Validation Column (Holdout Sample)

  Use to subset the data into a set used to build a model (training) and a set used to evaluate a model's predictive performance (validation). If multiple models are fit, the best performer on the validation data is often the one chosen. At times, a third set is used (test) to evaluate the chosen model's predictive performance on new data. This is considered to be a more accurate means to evaluate a...

  gail_massari

  Learning Library |

  Aug 20, 2025 8:36 PM

  55 views | 0 replies

• Naive Bayes

    Use this predictive modeling technique to predict a categorical outcome (classify) as a function of multiple predictor variables. The technique classifies observations by applying Bayes’ Theorem to conditional probabilities. Naive Bayes From an open JMP^® table, select Analyze > Predictive Modeling > Naive Bayes.Select a nominal or ordinal response variable from Select Columns and click Y, Respo...

  gail_massari

  Learning Library |

  Aug 20, 2025 8:29 PM

  77 views | 0 replies

• Master Machine Learning with Zero Code in JMP & JMP Pro

  Predictive modeling and machine learning are powerful tools for uncovering insights and making data-driven decisions. JMP and JMP Pro make these advanced techniques accessible to all users through point-and-click interface—no coding needed.   See how to: Build predictive models using decision trees, forests, and neural netsVisualizations for interpreting modelsValidate models and compare performa...

  gail_massari

  Learn JMP Events |

  Tuesday, August 19, 2025

• Optimizing Battery Storage for Renewable Energy Systems

  Video using JMP 18 was posted in August 2025.  Do you need to identify battery degradation patterns to help design or improve batteries for your renewable energy systems? Are you trying to identify optimal temperature or other parameters’ effect to prolong battery stability in your environment?    This session shows how to: Manage, organize and combine large electrochemical data for numerous batte...

  gail_massari

  Learn JMP Events |

  Friday, August 15, 2025

• Neural Networks

  Build a network based model to describe the impact that multiple predictor variables have on an outcome and to make predictions of a categorical outcome (classify) or a continuous outcome. Neural Networks From an open JMP^® data table, select Analyze > Predictive Modeling > Neural.Select a response variable from Select Columns and click Y, Response. Here we chose ‘Price’.Select explanatory variabl...

  gail_massari

  Learning Library |

  Aug 13, 2025 7:51 PM

  66 views | 0 replies

• K Nearest Neighbors

   Use a proximity-based algorithm to predict a categorical outcome (classify) or prediction the value of a continuous outcome for new observations based upon the outcomes of similar observations (i.e., their nearest neighbors). K Nearest Neighbors From an open JMP^® table, select Analyze > Predictive Modeling > K Nearest Neighbors.Select a categorical or continuous response variable from Select Col...

  gail_massari

  Learning Library |

  Aug 13, 2025 7:46 PM

  50 views | 0 replies

• Support Vector Regression

   Build a boundary based statistical model to predict a continuous outcome as a function of multiple predictor variables. SVR is able to create much more flexible boundary shapes than the Regression Tree (Partition) method. Support Vector Regression From an open JMP^® table, select Analyze > Predictive Modeling > Support Vector Machines.Add a continuous variable from Select Columns to the Y, ...

  gail_massari

  Learning Library |

  Aug 13, 2025 7:39 PM

  68 views | 0 replies

• Support Vector Machines - Classification

    Build a boundary based statistical model to predict a categorical outcome (classify) as a function of multiple predictor variables. SVM is able to create much more flexible boundary shapes than the Classification Tree (Partition) and Discriminant Analysis method. Support Vector Machines From an open JMP^® table, select Analyze > Predictive Modeling > Support Vector Machines.Add a nominal or ordi...

  gail_massari

  Learning Library |

  Aug 13, 2025 7:39 PM

  119 views | 0 replies

• Classification Trees (Partition)

  Use to build a partition-based model (Decision Tree) that identifies the most important factors that predict a categorical outcome (classify) and use the resulting tree to make predictions for new observations.
  Classification Trees From an open JMP^® table, select Analyze > Predictive Modeling > Partition.Select a nominal or ordinal response variable from Select Columns and click Y, Response.Selec...

  gail_massari

  Learning Library |

  Aug 7, 2025 5:34 PM

  111 views | 0 replies

• Time Series Forecasting

  The Time Series Forecast platform builds a variety of different exponential smoothing models and automatically selects the with the best forecast performance. The platform is designed to forecast multiple time series. Time Series Forecast From an open JMP^® data table, select Analyze > Specialized Modeling > Time Series Forecast.
  
  Select a continuous variable from Select Columns, and click Y (contin...

  gail_massari

  Learning Library |

  Aug 6, 2025 11:43 PM

  81 views | 0 replies

• Time Series Smoothing Models

  Use smoothing based time series models to describe patterns and forecast future time periods.  Smoothing Models From an open JMP^® data table, select Analyze > Specialized Modeling > Time Series.Select a continuous variable from Select Columns, and click Y, Time Series (continuous variables have blue triangles). Select a time and click X, Time ID (optional). Click OK.  
  - A time series graph of the...

  gail_massari

  Learning Library |

  Aug 6, 2025 11:31 PM

  96 views | 0 replies

• ARIMA Modeling

  Use ARIMA (Auto Regressive Integrated Moving Average) time series models to examine autocorrelation, describe patterns (trends and seasonality), and forecast future time periods.  ARIMA Modeling From an open JMP^® data table, select Analyze > Specialized Modeling > Time Series.Select a continuous variable from Select Columns, and click Y, Time Series (continuous variables have blue triangles). Sel...

  gail_massari

  Learning Library |

  Aug 6, 2025 11:26 PM

  94 views | 0 replies

• Boosting Your Renewable System Performance

  Video using JMP 18 was posted in August 2025. Do you need to make sense of sensor or other sequential measurement data where the responses are not single points, but a range of points presented as curves? Do you need to use that data to predict the likelihood of a system failure or maintain consistent power quality for a renewable energy or other systems? Would you like examples of how to use sof...

  gail_massari

  Learn JMP Events |

  Friday, August 1, 2025

• Advanced Analytics and Deep Learning in Chemistry

    With the JMP Student Edition, academic researchers get no-cost access to all JMP Pro capabilities, such as Python integration and more advanced tools for building machine learning models. This webinar will demonstrate advanced analytical applications for researchers in Chemistry, Chemical Engineering, Materials Science, and related fields by enhancing JMP Student Edition’s native features with...

  Ross_Metusalem

  Learn JMP Events |

  Wednesday, June 11, 2025

• JMP for Teaching Statistics in Semiconductor Engineering Education

      Get JMP software free for academic use at jmp.com/student   Semiconductor engineering is a data intensive process. Engineers need to visualize and analyze vast amounts of data to improve yield, identify and rectify drifts or failures, monitor process health, and more. JMP statistical software, used widely in the semiconductor industry, is a strong tool for teaching students in semiconductor ...

  Ross_Metusalem

  Learn JMP Events |

  Thursday, April 17, 2025

• Teaching Predictive Modeling

    Get JMP software free for academic use at jmp.com/student   Predictive modeling encompasses a range of techniques for using historical data to predict future outcomes, and it has applications in nearly all quantitative disciplines. The JMP Student Edition has robust predictive modeling capabilities, including a range of algorithms for classification and regression, automated model cross-valida...

  Ross_Metusalem

  Learn JMP Events |

  Thursday, April 3, 2025

• Removing the Guesswork for Handling Collinearity in Your Models - Mastering JMP

  Video was recorded in March 2025 using JMP 18 and JMP Pro 18.   Do you work with complicated processes where possible unaccounted-for variables might be impacting your predictions? Do you sometimes struggle to identify the validity of factors impacting the results of complicated interactions? Are collinear factors thwarting the efficiency of your process improvement projects?   In this Mastering J...

  gail_massari

  Learn JMP Events |

  Friday, March 28, 2025

• JMP for Teaching Data Analytics

    Get JMP software free for academic use at jmp.com/student   Data analytics courses are a core part of curricula in business, marketing, information systems, and related fields. These courses frequently cover a range of data analysis techniques, from basic data visualization and analysis through advanced methods that can include machine learning, time series forecasting, or text mining, to name...

  Ross_Metusalem

  Learn JMP Events |

  Thursday, March 20, 2025

• JMPをマスターしよう　「モデルのあてはめ」活用編　オンデマンド（日本語）

  JMPの「モデルのあてはめ」プラットフォームは、データに統計モデル（回帰、分散分析など）をあてはめ、目的変数と説明変数との関係性やパターンを分析できます。本セミナーでは、この「モデルのあてはめ」でできることに焦点をあて、基本的な操作方法、レポートの解釈、多重共線性について説明します。   ※2024年12月5日に実施した同タイトルのセミナーのオンデマンドバージョンです。内容を基本操作と多重共線性に絞ってオンデマンド化しています。下記のチャプター番号と動画のチャプター番号が対応していない箇所がございます。あらかじめください。 ※セミナー実施時点での最新バージョン「JMP 18.1.1」を使用しています。 ※オンデマンド版の配布資料をダウンロードできます（jmp-fitmodel-ondemand.pdf）。   目次 イントロダクション（3分32秒） Chapter1. 基本的な操作の流れと...

  Masukawa_Nao

  Learn JMP Events |

  Monday, January 6, 2025