Plan usage screen showing options included in current plan
Editing invited user
Key drivers in Binary Classification
Visual representation of most important Key drivers
Core visualizations for EDA
Import Demo Dataset screen
3\. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.
4\. Adjust your dataset options on the Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.
5\. To create a new model in the Graphite Note main menu click on "Models".
6\. You will get list of available models. Click on "New Model" to create a new one.
7\. Select model type from our templates. In our case, we will select "Regression" by double clicking on its name.
8\. Select dataset you want to use to produce a model. We will use "Demo-Ads.csv."
9\. Name your new model. We will call it "Regression on Demo-Ads".
10\. Write the description of the model and select tag. If you want you can also create a new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up a "Regression Model", first you will need to define the "Target Feature". That is a numeric column from your dataset that you'd like to make predictions about. In the case of Regression on Ads dataset, target feature is "Clicks" column.
13\. Click "Next" to get the list of model features that will be included in the scenario. Model relies upon each column (feature) to make accurate predictions. When training the model, we will calculate which of the features are most important and behave as Key Drivers.
14\. To start training the model, click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Regression model is trained. Click on "Performance" tab to get model insights and view Key Drivers.
16\. Explore the Regression Model by clicking on Impact Analysis, Model Fit and Training Results to get more insights on how the model is trained and set up.
17\. If you want to take turn model into action click on "Predict" tab in the main model menu.
18\. You can produce your own "What-If" analysis based on existing training results. You can also import a fresh CSV dataset into the data model, to make predictions on target column. In our case that is "Clicks". Keep in mind, the dataset you are uploading needs to contain same feature columns as your model.
19\. Use your model often to predict future behaviour, and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
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3\. Once selected, demo dataset will load into your account. Dataset view will automatically open.
4\. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.
5\. To create new model in the Graphite Note main menu click on "Models"
6\. You will get list of available models. Click on "New Model" to create new one.
7\. Select model type from our templates. In our case we will select "Binary Classification" by double clicking on its name.
8\. Select dataset you want to use to produce model. We will use "Demo-Churn.csv."
9\. Name your new model. We will call it "Binary Classification on Demo-Churn".
10\. Write description of the model and select tag. If you want to, you can also create a new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up Binary Classification model first you need to define "Target Feature". That is binary column from your dataset that you'd like to make predictions about. In case of Binary Classification on Churn dataset, the target feature will be "Churn" column.
13\. Click "Next" to get the list of model features that will be included in scenario. Model relies upon each column (feature) to make accurate predictions. When training model we will calculate which of the features are most important and behave as Key Drivers.
14\. To start training model click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for a few moments and Voilà! Your Binary Classification model is trained. Click on "Performance" tab to get model insights and view Key Drivers.
16\. Explore Binary Classification model by clicking on Impact Analysis and Training Results to get more insights on how model is trained.
17\. If you want to turn your model into action click on "Predict" tab in the main model menu.
18\. You can produce your own "What-If analysis" based on existing training results. You can also import a fresh CSV dataset with data model will use to make predictions on a target column. In our case that is "Churn". Keep in mind, the dataset you are uploading needs to contain same feature columns as your model.
19\. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
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3\. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.
4\. Adjust your dataset options on Settings tab. Click the Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.
5\. To create a new model in the Graphite Note main menu click on "Models"
6\. You will get list of available models. Click on "New Model" to create new one.
7\. Select model type from our templates. In our case we will select "Regression" by double clicking on its name.
8\. Select the dataset you want to use to produce the model. We will use "Demo-CO2-Car-Emissions-Canada.csv"
9\. Name your new model. We will call it "Regression on Demo-CO2-Car-Emissions"
10\. Write the model description and select tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up the Regression model first, you need to define "Target Feature". That is numeric column from your dataset that you'd like to make predictions about. In case of Regression on car emissions dataset target feature is "CO2 Emissions(g/km)" column.
13\. Click "Next" to get the list of model features that will be included into scenario. Model relies upon each column (feature) to make accurate predictions. When training the model, we will calculate which of the features are most important and behave as Key Drivers.
14\. To start training the model, click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Regression model is trained. Click on the "Performance" tab to get model insights and view the Key Drivers.
16\. Explore the Regression model by clicking on Impact Analysis and Training Results, to get more insights on how model is trained.
17\. If you want to take your model into action, click on the "Predict" tab in the main model menu.
18\. You can produce your own What-If analysis based on existing training results. You can also import a fresh CSV dataset that the model will use to make predictions on the target column. In our case that is "CO2 Emissions (g/km)". Keep in mind, the dataset you are uploading needs to contain same feature columns as your model.
19\. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
3\. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.
4\. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.
5\. To create new model in the Graphite Note main menu click on "Models"
6\. You will get list of available models. Click on "New Model" to create a new one.
7\. Select a model type from our templates. In our case we will select "Multi-Class Classification" by double clicking on its name.
8\. Select the dataset you want to use to produce model. We will use "Demo-Diamonds.csv"
9\. Name your new model. We will call it "Multi-Class Classification on Demo-Diamonds"
10\. Write description of the model and select tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up a Multi Class model first you need to define "Target Feature". That is text column from your dataset you'd like to make predictions about. In case of Multi Class on Diamonds dataset target feature is "Cut" column.
13\. Click "Next" to get the list of model features that will be included under the scenario. Model relies on each column (feature) to make accurate predictions. When training the model, we will calculate which of the features are most important and behave as Key Drivers.
14\. To start training model click "Run Scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Regression model is trained. Click on "Performance" tab to get model insights and view Key Drivers.
16\. Explore Multi Class model by clicking on Impact Analysis, Model Fit, Accuracy Overview or Training Results to get more insights on how model is trained and set up.
17\. If you want to take your model into action click on "Predict" tab in the main model menu.
18\. You can produce your own What-If analysis based on existing training results. You can also import a fresh CSV dataset to make predictions on target column. In our case that is "Cut". Keep in mind, the dataset you are uploading needs to contain the same feature columns as your model.
19\. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
3\. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.
4\. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore the dataset details on Summary tab.
5\. To create new model in the Graphite Note main menu click on "Models"
6\. You will get list of available models. Click on "New Model" to create new one.
7\. Select a model type from our templates. In our case we will select "RFM Customer Segmentation" by double clicking on its name.
8\. Select dataset you want to use to produce model. We will use "Demo-eCommerce-Orders.csv".
9\. Name your new model. We will call it "RFM customer segmentation on Demo-eCommerce-Orders".
10\. Write description of the model and select tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. Click this text field.
13\. To set up RFM model, you first need to identify and define few parameters. These are: "Time /Date Column", "Customer ID", "Customer Name" (optional) and "Monetary" (amount spent). In our case we will select "created\_at" as date, "user\_id" as customer and "total" as monetary parameter.
14\. To start training model click "Run scenario".
15\. Wait for a few moments and Voilà! Your RFM Customer Segmentation model is trained. Click on the "Results" tab to get model insights.
16\. You can navigate over different tabs to get deep insights into RFM analysis from different perspectives: Recency, Frequency, Monetary.
17\. Tab "RFM Scores" shows detailed explanation on different scores along with RFM segments and descriptions.
18\. Tab "RFM Analysis" gives you more details on different segments
19\. Tab "RFM Matrix" will show you number of customers belonging to different RFM segment. You can export matrix data to use Customer IDs for different business actions (e.g. exporting list of about to churn customers).
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3\. Once selected, the demo dataset will load directly to your account. The Dataset view will automatically open.
4\. Adjust your dataset options on the Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore the dataset details on the Summary tab.
5\. To create a new model in the Graphite Note main menu click on "Models"
6\. You will get list of available models. Click on a "New Model" to create new one.
7\. Select the model type from our templates. In our case, we will select "Regression" by double clicking on its name.
8\. Select the dataset you want to use to produce the model. We will use "Demo-Housing-Prices.csv"
9\. Name your new model. We will call it "Regression on Demo-Housing-Prices"
10\. Write the description of the model and select a tag. If you want to, you can also create new a tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up a Regression Model, firstly, you need to define the "Target Feature". That is a numeric column from your dataset that you'd like to make predictions about. In the case of Regression on Demo Housing Prices, the dataset target feature is "Price" column.
13\. Click "Next" to get the list of model features that will be included into scenario. Model relies upon each column (feature) to make accurate predictions. When training model we will calculate which of the features are most important and behave as Key Drivers.
14\. To start training the model, click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Regression model is trained. Click on the "Performance" tab to get model insights and view the Key Drivers.
16\. Explore the Regression Model by clicking on Impact Analysis, Model Fit and Training Results to get more insights on how model is trained and set up.
17\. If you want to take your model into action. Click on "Predict" tab in the main model menu.
18\. You can produce your own What-If analysis based on existing training results. You can also import fresh CSV dataset with data model will use to make predictions on the target column. In our case, that is "Price". Keep in mind, the dataset you are uploading needs to contain the same feature columns as your model.
19\. Use your model often to predict future behaviour, and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
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3\. Once selected, the demo dataset will load directly to your account. The Dataset view will automatically open.
4\. Adjust your dataset options on the Settings tab. Click Columns tab to view the list of available columns with their corresponding data types. Then explore the dataset details on Summary tab.
5\. Click "Models"
6\. You will get list of available models. Click on "New Model" to create a new one.
7\. Select the model type from our templates. In our case, we will **select "Binary Classification"** by double clicking on its name.
8\. Select dataset you want to use to produce the model. We will use "Demo-Lead-Scoring.csv."
9\. Name your new model. We will call it "Binary Classification on Demo-Lead-Scoring".
10\. Write the description of the model and select a tag. If you want to, you can also create a new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up a Binary Classification model, firstly, you need to define the "Target Feature". That is a binary column from your dataset that you'd like to make predictions about. In the case of Binary Classification on a Lead Scoring dataset, the target feature will be the "Converted" column.
13\. Click "Next" to get the list of model features that will be included in the model scenario. The model relies upon each column (feature) to make accurate predictions. When training the model, it will calculate which of the features are most important and behave as Key Drivers.
14\. To start training model click "Run Scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Binary Classification model is trained. Click on the "Performance" tab to get model insights and to view the Key Drivers.
16\. Explore the "Binary Classification" model by clicking on the Impact Analysis and Training Results to get more insights on how the model is trained.
17\. If you want to turn your model into action, click on "Predict" tab in the main model menu.
18\. You can produce your own What-If analysis based on existing training results. You can also import a fresh CSV dataset to make predictions on the target column. In our case that is "Converted". Keep in mind, dataset you are uploading needs to contain same feature columns as your model.
19\. Use your model often to predict future behaviour, and to learn which key drivers are impacting outcomes. The more you use and retrain your model, the smarter it becomes!
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3\. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.
4\. Adjust your dataset options on the Settings tab. Click the Columns tab to view the list of available columns, with their corresponding data types. Explore the dataset details on Summary tab.
5\. To create a new model in the Graphite Note main menu, click on "Models"
6\. You will get list of available models. Click on the "New Model" to create a new one.
7\. Select the model type from our templates. In our case, we will select "General Segmentation" by double clicking on its name.
8\. Select the dataset you want to use to produce model. We will use "Demo-Mall-Customers.csv"
9\. Name your new model. We will call it "General Segmentation on Demo-Mall-Customers".
10\. Write a description of the model and select a tag. If you want to, you can also create new tag from the pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up your General Segmentation model, firstly, you need to define "Feature" columns. That is numeric column (or columns) from your dataset on which segmentation would be based. In the case of General Segmentation on Mall Customers dataset, the numeric feature will be "Age", Annual Income" and "Spending Score" columns.
13\. To start training the model click "Run Scenario". This will train your model based on the uploaded dataset.
14\. Wait for few moments and Voilà! Your General Segmentation model is trained. Click on "Results" tab to get model insights and explore segmentation clusters.
15\. Navigate over different tabs to get insights from high level "Cluster Summary" to "By Cluster" charts and tables.
16\. Use "Cluster Visualisations" to view the scatter plot visualisations of cluster members.
3\. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.
4\. Adjust your dataset options on the Settings Tab. Click the Columns tab to view list of available columns with their corresponding data types. Explore dataset details on the Summary tab.
5\. To create a new model in the Graphite Note main menu click on "Models".
6\. You will get list of available models. Click on "New Model" to create a new one.
7\. Select your model type from our templates. In our case we will select "Regression" by double clicking on its name.
8\. Select dataset you want to use to produce the model. We will use "Demo-MMM.csv"
9\. Name your new model. We will call it "Regression on Demo-MMM".
10\. Write description of the model and select tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up your "Regression Model", firstly, you need to define "Target Feature". That is numeric column from your dataset that you'd like to make predictions about. In the case of **Regression on Marketing Mix and Sales Dataset**, the target feature is "Sales" column.
13\. Click "Next" to get the list of model features that will be included in scenario. The model relies upon each column (feature) to make accurate predictions. When training the model, we will calculate which of the features are most important and behave as Key Drivers.
14\. To start training the model, click "Run Scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Regression model is trained. Click on "Performance" tab to get model insights and view the Key Drivers.
16\. Explore Regression model by clicking on Impact Analysis, Model Fit and Training Results to get more insights on how the model is trained and set up.
17\. If you want to take your model into action, click on "Predict" tab in the main model menu.
18\. You can produce your own What-If analysis based on existing training results. You can also import fresh CSV dataset with data model will use to make predictions on target column. In our case, that is "Sales". Keep in mind, dataset you are uploading needs to contain same feature columns as your model.
19\. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
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3\. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.
4\. Adjust your dataset options on the Settings tab. Click the Columns tab to view the list of available columns with their corresponding data types. Explore the dataset details on the Summary tab.
5\. To create a new model in the Graphite Note main menu, click on "Models".
6\. You will get list of available models. Click on "New Model" to create new one.
7\. Select the model type from our templates. In our case, we will select "Timeseries Forecast" by double clicking on its name.
8\. Select the dataset you want to use to produce the model. We will use "Demo-Monthly-Car-Sales.csv".
9\. Name your new model. We will call it **"Timeseries forecast on Demo-Monthly-Car-Sales".**
10\. Write description of the model and select a tag. If you want to, you can also create new tag from pop-up "Tags" window, that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up Timeseries forecast analysis first you need to define the "Target Column". That is a numeric column from your dataset that you'd like to forecast. In the case of Timeseries on monthly car sales dataset target column is "Sales"
13\. If dataset includes multiple time series sequences, you can select field that will be used to uniquely identify each sequence. In the case of our demo dataset, we will not apply Sequence Identifier field since we have only "Sales" target column.
14\.
15\. Click "Next" to open "Time/Date Column" selection. Choose "Month" as date column.
16\. From additional options below, choose "Monthly" as time interval and define "Forecast Horizon". We will set up forecast horizon to 6 months in the future.
17\. Click "Next" to activate "Seasonality" options step. Here, you can define seasonality specifics of your forecast. If time interval is set to daily on the next step you will also have "Advanced options" available.
18\. Click "Run Scenario" to train your timeseries forecast.
19\. Wait for a few moments and Voilà! Your Timeseries forecast is trained. Click on the "Performance" tab to get insights and view the graph with original(historical) and predicted model data.
20\. Explore more details on "trend", "Seasonality" and "Details" tabs.
21\. If you want to turn your model into action click on "Predict" tab in the main model menu.
22\. You can produce your own Forecast analysis based on the existing training results by selecting Start and End date from drop down calendar and clicking on "Predict" button.
23\. Use your model often to predict future sales results. The more you use and retrain your model, the smarter it becomes!
3\. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.
4\. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.
5\. To create a new model in the Graphite Note main menu, click on "Models".
6\. You will get list of available models. Click on "New Model" to create new one.
7\. Select model type from our templates. In our case we will select "Regression" by double clicking on its name.
8\. Select the dataset you want to use to produce model. We will use "Demo-Store-Item-Demand.csv".
9\. Name your new model. We will call it "Regression on Demo-Store-Item-Demand".
10\. Write a description of the model and select tag. If you want to, you can also create new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up **Regression Model**, firstly, you will need to define "Target Feature". That is the numeric column from your dataset that you'd like to make predictions about. In case of Regression on Store Item Demand dataset target feature is "Sales" column.
13\. Click "Next" to get the list of model features that will be included in model scenario. The model relies on each column (feature) to make accurate predictions. When training a model, we will calculate which of the features are most important and behave as the Key Drivers.
14\. To start training your model click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Regression model is trained. Click on "Performance" tab to get model insights and view Key Drivers.
16\. Explore Regression model by clicking on Impact Analysis, Model Fit and Training Results to get more insights on how model is trained and set up.
17\. If you want to take your model into action click on "Predict" tab in the main model menu.
18\. You can produce your own What-If analysis based on existing training results. You can also import a fresh CSV dataset to make predictions on target column. In our case that is "Sales". Keep in mind, dataset you are uploading needs to contain same feature columns as your model.
19\. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
3. After selection, the demo dataset will automatically load into your account and the dataset view will open immediately.
4\. Adjust your dataset options on the Settings tab. Click Columns tab to view the list of available columns with their corresponding data types. Explore dataset details on Summary tab.
5\. To create a new model in the Graphite Note main menu, click on "Models"
6\. You will get a list of available models. Click on "New Model" to create a new one.
7\. Select the model type from our templates. In our case we will select "Binary Classification" by double clicking on its name.
8\. Select the dataset you want to use to produce model. We will use "Demo-Upsell.csv".
9\. Name your new model. We will call it "Binary Classification on Demo-Upsell".
10\. Write a description of the model and select a tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up a Binary Classification model, firstly, you need to define "Target Feature". That is binary column from your dataset that you'd like to make predictions about. In case of Binary Classification on Upsell dataset target feature will be "Applied" column.
13\. Click "Next" to get the list of model features that will be included in the model scenario. Your model relies upon each column (feature) to make accurate predictions. When training the model we will calculate which of the features are most important and behave as Key Drivers.
14\. To start training your model click "Run scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Binary Classification model is trained. Click on "Performance" tab to get model insights and view the Key Drivers.
16\. Explore Binary Classification model by clicking on Impact Analysis and Training Results to get more insights on how model is trained.
17\. If you want to take your model into action click on "Predict" tab in the main model menu.
18\. You can produce your own What-If analysis based on existing training results. You can also import a fresh CSV dataset to make predictions on the target column. In our case that is "Applied". Keep in mind, the dataset you are uploading needs to contain the same feature columns as your model.
19\. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
\ Settings
Columns
View Data
Summary
Association
Option 1: Create Dataset using homepage
Option 2: Create Dataset from the Dataset List page
Choose CSV file
Name and Description of the dataset
Parsing options
Option 1: Create Dataset using homepage
Option 2: Create Dataset from the Dataset List page
Select MySQL as your dataset source
Name and Description for the dataset
Option 1: Create Dataset using homepage
Option 2: Create Dataset from the Dataset List page
Select MariaDB as your dataset source
Name and Description for the dataset
Option 1: Create Dataset using homepage
Option 2: Create Dataset from the Dataset List page
Select PostgreSQL as your dataset source
Name and Description for the dataset
Option 1: Create Dataset using homepage
Option 2: Create Dataset from the Dataset List page
Select Redshift as your dataset source
Name and Description for the dataset
Option 1: Create Dataset using homepage
Option 2: Create Dataset from the Dataset List page
Option 1: Create Dataset using homepage
Option 2: Create Dataset from the Dataset List page
Name and Description for the dataset
Features not fit for model
Preprocessing steps
Timeseries forecast model
Time and Date selections and Regressors
Seasonality settings
Selecting advanced parameters like special dates and holidays
Selecting Country Holidays
Running scenario
Overview in Model performance tab
Seasonality graph showing patterns in historical data
Special Dates and Holidays effects
Model results in a dataset that can be exported to Excel
Predict option with what-if selections
Predicted data for selected interval
API request definition that can be used to set up API call from third party tool
Binary Classification
Advanced parameters setup screen
Actionable Insights goal settings
Preprocessing steps
Model training complete
Model Overview with Health check details
Key Drivers in Binary Classification
Strategic Summary in Actionable Insights
Predict by uploading CSV
Predict by using API
Multiclass Classification
Selecting target feature in Multiclass model scenario
Selecting model features
Advanced parameters setup screen
Actionable Insights goal settings
Preprocessing steps
Model training complete
Evaluation metrics showing overall model performance
Model Overview with health check details
Key drivers in Multiclass Classification model
Impact analysis in Multiclass Classification model
Comparison of correct and incorect predictions in Model Fit section
Confusion Matrix in Multiclass Classification
Description of Model Metrics
Training results with model Hyper Parameters
Model results in a dataset that can be exported to Excel
Strategic Summary in Actionable Insights
Predict option with what-if selections
Predict by uploading CSV
Predict by using API
Regression model
Selecting target feature in Regression model scenario
Selecting model features
Advanced parameters setup screen
Actionable Insights goal settings
Model training completed
Model Overview with Health Check
Key drivers in Regression model
Impact Analysis in Regression model
Model fit in Regression model
Training results with model Hyper Parameters
Details tab in Regression model
Actionable Insights in Regression with Strategic Summary and Feature Insights
Predict option with what-if selections
Predict by uploading CSV
Predict by using API
General Segmentation
Cluster summary tab
Summary table
Explore by Cluster tab
Explore by Numeric Value tab
Summary table
Summary table
Summary table
Summary table
Model scenario settings
Overview tab with high level summary and KPIs
The forecasted number of purchases chart in CLV Insights
The forecasted amount chart in CLV Insights
Theaverage alive probability chart in CLV Insights
Customer Segmentation Based on Probability Alive in Segments tab
Customer Cohort Analysis
Cohort analysis model setup
Customer Cohort Analysis Results
Results representation in Customer Cohorts
Percentage metric view
Repeat by with different Categories that are shown separately
Customer Cohort Analysis details tab
Choosing parameters for ABC Analysis scenario
New vs Returning Customers
Chart showing New and Returning customers in a period in a monthly representation
Chart showing New and Returning customers in a period in a daily representation
Chart showing the percentage of returning customers in a period in a monthly representation
Chart showing the revenue spent depending if they are new customers or not in a period in a monthly representation
Details tab
Actionable Insights definition in model Scenario
Actionable Insights with Strategic Summary tab
Algorithms you can run for Regression
Algorithms you can run for Binary and Multiclass Classification
Sorting option in Classification models
Probability Threshold option
Remove Multicollinearity option
Multicollinearity Threshold option
Enable model tuning option
Remove outliers option
Outliers Threshold option
Model Overview on Regression
Performance KPIs on Binary Classification model
Performance KPIs on Regression model
Performance KPIs on Timeseries model
Combination Graph
Combination Graph Infos
Combination Graph example
Table
Table example
Pie Chart
Pie Chart Infos
Pie Chart Example
Scatter Chart
Scatter Chart Infos
Scatter Chart Example
APIs available in Graphite Note environment
Prediction API marked in red