Models
Saving & loading a Data Science Lab model
Last updated
Saving & loading a Data Science Lab model
Last updated
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. to get redirected to the quick start flow page.
