Welcome
User Guide
Model Training
Basic Configuration
This section provides the option to select more than one algorithm based on problem type and also provides the option to discard the unnecessary features from the dataset.
Problem Description

Release Notes
Installation Guide
Developer Guide
MLAC User Guide
User Guide
- Introduction
  Marketing AION (Artificial Intelligence ON) is an AI life cycle management platform for applying machine learning to real-world problems. Marketing AION encompasses the complete pipeline from raw data ingestion to a deployable machine learning model with a less-code/no-code approach.
- Model Management
- Model Training
  - Data Upload
    Data upload tab is used to load the dataset in the GUI to train the model and get insight of it.
  - Basic Configuration
    This section provides the option to select more than one algorithm based on problem type and also provides the option to discard the unnecessary features from the dataset.
    - Problem Description
    - Model Features
      This section provides the option to select features that will be used for training, options to change the data type and transform each selected feature.
    - Row Filtering and Grouping
      This section is used to filter certain rows from the dataset based on the requirement. If filtering is not required, this section can be skipped.
  - Updating Advanced Configuration
    To update the Advanced Configuration, complete the following steps:
  - Training the Model
    Model training tab gives the information about the Problem type, Selected algorithms, number of rows and Training Features, Training Data, Data balancing and Number of iterations.
- Model Prediction
  Model prediction predicts the input data based on the pattern of training data.
- Trusted AI
  This feature is available for classification and regression problem types.
- Model Monitoring
  This feature is available for classification and regression problem types.
- Utility
  For Data Generator and BYOM (Bring Your Own Model), select the Utility tab.

Problem Description

Select the Problem Type. The following problem types are supported:
1. Classification
2. Regression
3. Anomaly Detection
4. Time Series Anomaly Detection
5. Clustering
6. Time Series Forecasting
Note: By default, classification is selected.
Select the Scoring Criteria.
Based on the problem Type, select the Algorithms.

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