Accenture Interview Question Data Engineers
Country: United States
Interview Type: In-Person
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In data science, a parameter is a value that can be used to control the behavior of a model or algorithm. Parameters can be used to tune the model to improve its performance or to make it more generalizable.
- abhishekpeddi123 August 31, 2023There are two main types of parameters: input parameters and output parameters.
Input parameters are values that are passed to the model or algorithm when it is run. These parameters can be used to control the features that are used in the model, the number of iterations that the model runs, or the complexity of the model.
Output parameters are the values that are produced by the model or algorithm. These parameters can be used to make predictions, to classify data, or to cluster data.
Parameters can be linked to datasets in order to make the model or algorithm more reusable. When a parameter is linked to a dataset, the value of the parameter is automatically set to the value of the corresponding column in the dataset. This makes it easy to use the same model or algorithm with different datasets.
To create parameters for data sources, you can use the create_parameter() function. This function takes two arguments: the name of the parameter and the type of the parameter. The type of the parameter can be one of the following:
string
integer
float
boolean
For example, the following code creates a parameter called file_path of type string:
parameter = create_parameter("file_path", "string")
To create parameters for file paths, you can use the create_file_path_parameter() function. This function takes two arguments: the name of the parameter and the directory where the file is located.
For example, the following code creates a parameter called file_path of type string and sets the directory to the current working directory:
parameter = create_file_path_parameter("file_path", os.getcwd())