https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/Recent content in ML Models onHugo -- gohugo.ioSun, 11 Dec 2022 19:37:55 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/about_models/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/about_models/Models page lists all the models which are trained through Gathr. To know about the machine learning algorithms supported by Gathr, see Data Science → The information displayed and actions that can be performed on the listed models are explained below: Field Description Name Name of the registered model. Types Shows the type of the model. Date Created Created date and time for the trained model. This field will be empty for a failed model.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/data_science/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/data_science/At times you may need to derive insights from structured or unstructured data. With data science, you can interpret data for decision-making to provide meaningful information from volumes of data. Data science is a field that comprises everything related to data cleansing, preparation, and analysis. It is the method used to extract insights and information from data. Gathr incorporates data science techniques for deriving meaningful information from data. Using Gathr’s Machine Learning (ML) processors, one can train models and score models for streaming and batch data.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/register-models/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/register-models/Users can register ML Models in Gathr from the Models > ML tab. Click on the Register Model button to get started. Model Registration # The fields and procedures necessary for registering a machine learning model are explained below. Fields for Model Registration # Field Description Name The name of the model to be registered. Model API The chosen Machine Learning API, e.g., ML. Feature List A list of features used to train the model, which can be entered manually or uploaded via a .https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_feature_selection/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_feature_selection/For using analytics processor in both training and prediction mode, you have to explicitly specify Input Labels and Variables such as Continuous, Categorical and Text. 👉 In case of Isotonic Regression, specify Input Label and Continuous Variable. In case of K-Means, Input Label is not required, since it is used for clustering issues. Provide configuration details for Feature Selection as described below: Input Label: Input Label signifies the incoming message field, which will be considered as a label field for model training.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_pre-processing/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_pre-processing/In Pre-Processing, the data is transformed or consolidated so that the resulting mining process is more efficient, and the patterns found are easier to understand. Once features are selected on Features selection tab, you can apply various transformations using Pre-Processing tab. All ML models require feature column to be Vector Data type. For transforming raw input fields into type Vector, use Pre-Processing transformations. Following are the descriptions of all the transformations/algorithms supported by Gathr over various analytics processor.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_configuration/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_configuration/The configuration page is standard for all the ML Models except Tree-based, Naive Bayes, and Logistic Regression models. For the rest of the models, the properties are as follows. Operation: Type of operation to be performed by the Analytics processor. Training: Select the option training, if you want to train new models. Prediction: Select the option prediction, if you want to give predictions over existing model. Algorithm Type: Specifies whether the current algorithm is used for solving a classification problem or regression.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_post-processing/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_post-processing/The post-processing tab enables you to perform transformations on model output before displaying the final result. Currently, Gathr supports only one algorithm i.e IndexToString for post-processing. IndexToString # IndexToString maps a column of label indices back to a column containing the original labels as strings. A common use case is to produce indices from labels with StringIndexer, train a model with those indices and retrieve the original labels from the column of predicted indices with IndexToString.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_evaluation/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_evaluation/Evaluate models on the metrics available for the ML algorithm. Model Evaluation is configured using the following three properties: Enable Model Evaluation: Select the box for Enabling the model for evaluation. Train Ratio: Ratio in which incoming data will be split for training and testing. Value should be between 0 and 1. Example – 0.7 (70% data will be used for training and 30% for testing) Select Metric: The metric on which user wants to evaluate the model.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_hyper_parameters/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/ml-models-data-science/model_hyper_parameters/Using this tab, you are able to optimize hyper parameters of algorithms used in the transformation chain. Execute Tuning: When selected, enables model tuning and evaluation. Validation Type: Tools used for tuning the model: Cross validation: In cross-validation, you make a fixed number of folds (or partitions) of the data, run the analysis on each fold, and then average the overall error estimate. Train Validation Split: Train Validation Split creates a single dataset pair.