Linear Regression parameters

Overview:

Linear regression is a fundamental technique for modeling the relationship between a dependent variable and one or more independent variables. OtasML provides several configuration options to fine-tune linear regression models for optimal performance. Below is a comprehensive guide to these parameters.

Configurations page:

The Configurations page allows users to adjust various parameters of the linear regression model. Here are the details:

Intercept

• Default Value: True
• Description: This parameter is a boolean parameter that determines whether or not the model should fit an intercept term, also known as the bias or constant term.
• When fit_intercept is set to True, the model will estimate both the slope (coefficients) of the independent variables and the intercept term.
• When fit_intercept is set to False, the model will not estimate an intercept term.

Copy x

• Default Value: True
• Description: Controls whether a copy of the input data (independent variables) should be made before fitting the model.

N jobs

• Default Value: None
• Description: The number of jobs to use for computation. Provides speedup for sufficiently large problems.
• Warning: -1 means using all processors.

Positive

• Default Value: False
• Description: When set to True, forces the coefficients to be positive. This option is only supported for dense arrays.

Shuffle

• Default Value: False
• Description: It refers to the practice of randomizing the order of the training data before using it to fit the model. This is typically done to introduce a level of randomness and prevent any biases that might arise from the ordering of the data.

Test size

• Default Value: 0.2
• Description: The test size parameter is used when splitting the dataset into these subsets, and it specifies the portion of the data that will be used for testing.

Train size

• Default Value: 0.8
• Description: The train size parameter is used when splitting the dataset into these subsets, and it specifies the portion of the data that will be used for model training.

Conclusion

OtasML’s configuration options for linear regression models allow users to tailor their models to specific datasets and prediction tasks. Adjusting parameters such as intercept, copy x, and shuffle can significantly impact the model’s performance and behavior. Use this guide to fine-tune your linear regression models and achieve superior predictive accuracy with OtasML.