Tellius
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      • Vizpads (Explore)
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    • ⬇️Initial Setup
      • Tellius architecture
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  • Kaiya
    • ♟️Understanding AI Agents & Agentic Flows
      • Glossary
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      • Mastering Kaiya conversational AI
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  • 🔍Search
    • 👋Get familiar with our Search interface
    • 🤔Understanding Tellius Search
    • 📍Search Guide
    • 🚀Executing a search query
      • Selecting a Business View
      • Typing a search query
      • Constructing effective search queries
      • Marketshare queries
    • 🔑Analyzing search results
      • Understanding search results
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      • Time taken to execute a query
      • Interacting with the resulting chart
    • 📊Know your charts in Tellius
      • Understanding Tellius charts
      • Variations of a chart type
      • Building charts from Configuration pane
      • List of chart-specific fields
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      • Advanced Analytics
      • Cumulative line chart
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    • 🕵️‍♂️Search history
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  • 📈Vizpads (Explore)
    • 🙋Meet Vizpads!
    • 👋Get familiar with our Vizpads
    • #️⃣Measures, dimensions, date columns
    • ✨Creating Vizpads
    • 🌐Applying global filters
      • Filters in multi-BV Vizpads
      • Filters using common columns
    • 📌Applying local filters
    • 📅Date picker in filters
      • Customizing the calendar view
    • ✅Control filters
      • Multi-select list
      • Single-select list
      • Range slider
      • Dropdown list
    • 👁️Actions in View mode
      • Interacting with the charts
    • 📝Actions in Edit mode
      • 🗨️Viz-level actions
    • 🔧Anomaly management for line charts
      • Instance level
      • Vizpad level
      • Chart level
    • ⏳Time taken to load a chart
      • Instance level
      • Vizpad level
      • Chart level
    • ♟️Working with sample datasets
    • 🔁Swapping Business View of charts
      • Swapping only the current Vizpad
      • Swapping multiple objects
      • Configuring the time of swap
    • 🤖Explainable AI charts
  • 💡Insights (Discover)
    • 👋Get familiar with our Insights
    • ❓Understanding the types of Insights
    • 🕵️‍♂️Discovery Insights
      • Impact Calculation for Top Contributors
    • ➕How to create new Insights
      • 🔛Creating Discovery Insight
      • 🔑Creating Key Driver Insights
      • 〰️Creating Trend Insights
      • 👯Creating Comparison Insights
    • 🧮The art of selecting columns for Insights
      • ➡️How to include/exclude columns?
  • 🔢Data
    • 👋Get familiar with our Data module
    • 🥂Connect
    • 🪹Create new datasource
      • Connecting to Oracle database
      • Connecting to MySQL database
      • Connecting to MS SQL database
      • Connecting to Postgres SQL database
      • Connecting to Teradata
      • Connecting to Redshift
      • Connecting to Hive
      • Connecting to Azure Blob Storage
      • Connecting to Spark SQL
      • Connecting to generic JDBC
      • Connecting to Salesforce
      • Connecting to Google cloud SQL
        • Connecting to a PostgreSQL cloud SQL instance
        • Connecting to an MSSQL cloud SQL instance
        • Connecting to a MySQL Cloud SQL Instance
      • Connecting to Amazon S3
      • Connecting to Google BigQuery
        • Steps to connect to a Google BigQuery database
      • Connecting to Snowflake
        • OAuth support for Snowflake
        • Integrating Snowflake with Azure AD via OAuth
        • Integrating Snowflake with Okta via OAuth
        • Azure PrivateLink
        • AWS PrivateLink
        • Best practices
      • Connecting to Databricks
      • Connecting to Databricks Delta Lake
      • Connecting to an AlloyDB Cluster
      • Connecting to HDFS
      • Connecting to Looker SQL Interface
      • Loading Excel sheets
      • 🚧Understanding partitioning your data
    • ⏳Time-to-Live (TTL) and Caching
    • 🌷Refreshing a datasource
    • 🪺Managing your datasets
      • Swapping datasources
    • 🐣Preparing your datasets
      • 🤾Actions that can be done on a dataset
      • Data Pipeline
      • SQL code snippets
      • ✍️Writeback window
      • 🧩Editing Prepare → Data
      • Handling null or mismatched values
      • Metadata view
      • List of icons and their actions
        • Functions
        • SQL Transform
        • Python Transform
        • Standard Aggregation
        • Creating Hierarchies
      • Dataset Scripting
      • Fusioning your datasets
      • Scheduling refresh for datasets
    • 🐥Preparing your Business Views
      • 🌟Create a new Business View
      • Creating calculated columns
      • Creating dynamic parameters
      • Scheduling refresh for Business Views
      • Setting up custom calendars
    • Tellius Engine: Comparison of In-Memory vs. Live Mode
    • User roles and permissions
  • Feed
    • 📩What is a Feed in Tellius?
    • ❗Alerts on the detection of anomalies
    • 📥Viewing and deleting metrics
    • 🖲️Track a new metric
  • Assistant
    • 💁Introducing Tellius Assistant
    • 🎤Voice-based Assistant
    • 💬Interacting with Assistant
    • ↖️Selecting Business View
  • Embedding Tellius
    • What you should know before embedding
    • Embedding URL
      • 📊Embedding Vizpads
        • Apply and delete filters
        • Vizpad-related actionTypes
        • Edit, save, and share a Vizpad
        • Keep, remove, drill sections
        • Adding a Viz to a Vizpad
        • Row-level policy filters
      • 💡Embedding Insights
        • Creating and Viewing Insights
      • 🔎Embedding Search
        • Search query execution
      • Embedding Assistant
      • 🪄Embedding Kaiya
      • Embedding Feed
  • API
    • Insights APIs
    • Search APIs
    • Authentication API (Login API)
  • ✨What's New
    • Release 5.4
      • Patches 5.4.0.1 to 5.4.0.4
      • Patch 5.4.0.5
      • Patch 5.4.1
    • Release 5.3
      • Patch 5.3.1
      • Patch 5.3.2
      • Patch 5.3.3
    • Release 5.2
      • Patch 5.2.1
      • Patch 5.2.2
    • Release 5.1
      • Patch 5.1.1
      • Patch 5.1.2
      • Patch 5.1.3
    • Release 5.0
      • Patch 5.0.1
      • Patch 5.0.2
      • Patch 5.0.3
      • Patch 5.0.4
      • Patch 5.0.5
    • Release 4.3 (Fall 2023)
      • Patch 4.3.1
      • Patch 4.3.2
      • Patch 4.3.3
      • Patch 4.3.4
    • Release 4.2
      • Patch 4.2.1
      • Patch 4.2.2
      • Patch 4.2.3
      • Patch 4.2.4
      • Patch 4.2.5
      • Patch 4.2.6
      • Patch 4.2.7
    • Release 4.1
      • Patch 4.1.1
      • Patch 4.1.2
      • Patch 4.1.3
      • Patch 4.1.4
      • Patch 4.1.5
    • Release 4.0
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  1. Getting Started
  2. Best Practices

Predict

Pointers to ace your Models game

Tellius offers various types of machine learning models, including regression, classification, time series regression, and clustering. Each model comes with its own unique characteristics and applications.

  • Ensure the granularity of the data matches the desired outcome for predictions. If you intend to forecast monthly revenue for five different regions, you cannot use transaction-level data because the granularity of transaction data is not the same as data aggregated at the month and region levels. Aggregation functions or SQL/Python modules can help achieve the desired granularity of data.

  • Create a separate Business View for training and testing. This allows you to explicitly control for the logic used to separate data used for model training and evaluation. It is common to use mutually exclusive date ranges for these datasets where time is an important factor in the model.

  • Understand the business value that the model will help with. This is important for identifying the most appropriate model evaluation metric. Accuracy can be a misleading metric for model performance if the target classes are very imbalanced. AUROC is a more appropriate metric in that situation.

  • Use the Tellius platform to handle nulls and basic feature transformations for things like one-hot encoding.

  • Select multiple algorithms that align with your use case to identify the best-performing model. These selections are available specifically for Point-N-Click models, whereas AutoML automatically does this algorithm selection for a user.

  • If interpretability is very critical to the business problem you are solving with a model, choosing a neural network model will make that challenging. Logistic regression or ensemble tree algorithms would be a more appropriate fit in that situation.

  • Check the Notifications page to verify your models were trained successfully.

  • Save the trained model object once the training process has been completed.

  • Apply the model to the appropriate Business Views so your predictions are available for analysis by selecting the ellipses on the saved model object. The predictions are available in the Business View “Predictions” tab.

  • Save/Move Models into Projects to organize content that can easily be shared with users or user groups.

PreviousInsights (Discover)NextData

Last updated 1 year ago

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