Models
Saving & loading a Data Science Lab model
Last updated
Saving & loading a Data Science Lab model
Last updated
Check-out the walk-through on how to save and load a DSL Model.
Once the Notebook script is executed successfully, the users can save them as a model. The saved model can be loaded into the Notebook.
Navigate to a Notebook.
Write code using the following sequence:
Read DataFrame
Define test and train data
Create a model
Execute the script.
Get a new cell.
Give a model name to specify the model.
Execute the cell.
After the code gets executed, click the Save Model notebook in a new cell.
The saved model gets listed under the Models list.
Model - Trained model variable name.
ModelName - Desired name given by user for the trained model.
ModelType - Type in which model can be saved.
X - This array contains the input features or predictors used to train the model. Each row in the X_train array represents a sample or observation in the training set, and each column represents a feature or variable.
y - This array contains the corresponding output or response variable for each sample in the training set. It is also called the target variable, dependent variable, or label. The y_train array has the same number of rows as the X_train array.
estimator_type - The estimator_type of a data science model refers to the type of estimator use.
Click on a new cell and select the model by using the given checkbox to load it.
The model gets loaded into a new cell.
Please Note: Refer the Data Science Lab Quick Start Flow page to get an overview of the Data Science Lab module in nutshell. Click here to get redirected to the quick start flow page.
